FDE PRO by Cloud Soft Solutions
AI Forward Deployed Engineer (FDE) Course in Hyderabad
Learn to take an AI solution from a customer’s problem to production. Discovery, architecture, RAG, agents, MCP, enterprise integration, cloud deployment, security and measurable business value, in twelve weeks of hands-on training.
- Course fee
- ₹30,000
- Duration
- 3 months, 120+ hours
- Format
- Classroom in Ameerpet or live online
- You build
- 5 enterprise projects + a capstone
- Understand Discovery with stakeholders: the real problem and how success is measured.
- Design Architecture, data flow, security model and trade-offs.
- Build Production Python, APIs, RAG and agents.
- Integrate Enterprise systems, MCP tools and the customer’s identity.
- Deploy Containers, Kubernetes, Terraform and CI/CD.
- Observe Traces, metrics, evaluation scores and cost per request.
- Improve Fix, tune, harden and cut cost.
- Deliver value ROI, executive demo and a clean handover.
What is a Forward Deployed Engineer?
A Forward Deployed Engineer (FDE) is a software engineer who works directly with customers to turn their business problems into working, production-grade solutions. In AI, an FDE runs discovery, designs the architecture, builds with LLMs, RAG and agents, integrates enterprise systems, deploys to the cloud and proves measurable business value.
The role was popularized by Palantir, whose engineers were “forward deployed” to work inside customer organizations. Today AI companies, enterprise software platforms and consulting firms use FDEs to close the gap between a promising AI demo and a system the business actually runs on.
An FDE is not simply a developer and not simply a consultant. An FDE takes an ambiguous business problem, converts it into a technical solution, builds it quickly, integrates it with enterprise systems, deploys it into the customer’s environment, measures how well it performs and stays with it until it runs in production.
Why companies are hiring FDEs now
Building an AI demo takes an afternoon. Getting it into production inside a bank or a hospital takes discovery, integration, identity, security reviews, evaluation and someone who owns the outcome. That gap between a promising pilot and a system the business runs on is where Forward Deployed Engineers work.
Messy reality
The data lives in ServiceNow, SAP, SharePoint and spreadsheets, behind old APIs, proxies and firewalls.
Security says no
SSO, least privilege, private networking, audit trails and prompt-injection defenses must be in place before go-live.
No proof of value
Without evaluation scores and ROI numbers, pilots never get funded for production.
Current FDE job descriptions ask for exactly this mix: Python, LLMs, RAG and agents, enterprise integration, cloud deployment, security, evaluation and customer discovery, with ownership from the first conversation to post-go-live support.
FDE vs software engineer, solutions engineer and other roles
The FDE role overlaps with many familiar jobs. What makes it different is ownership: an FDE is customer-facing, writes production code and is accountable for the business result in production.
Scroll sideways to see every column.
| Role | Main job | Customer-facing | Writes production code | Owns |
|---|---|---|---|---|
| Software Engineer | Build product features | Rarely | Yes | The feature |
| AI / ML Engineer | Build and tune models and pipelines | Sometimes | Yes | Model quality |
| Solutions Engineer (pre-sales) | Demo the product and win the deal | Always | Prototypes | The sale |
| Solutions Architect | Design the target architecture | Often | Sometimes | The design |
| DevOps / SRE | Run platforms reliably | Rarely | Yes, infrastructure | Uptime |
| Consultant | Recommend what to do | Always | Rarely | The recommendation |
| Forward Deployed Engineer | Make it work in the customer’s world | Always | Yes | The business result in production |
What makes FDE PRO different from a typical AI course
Most AI courses teach a tool list and stop at a demo. FDE PRO makes the customer engagement lifecycle the backbone, and teaches every technology through the customer problem it solves.
Built around the customer, not the tool list
The customer engagement lifecycle is the backbone. Every technology is taught through the customer problem it solves.
Production, not demos
Identity, security reviews, evaluation, observability, cost and incident response are part of every project.
A weekly Customer Engagement Lab
Twelve labs of discovery calls, design documents, security reviews, RCAs, SOWs and executive demos.
You graduate with a delivery kit
Your FDE Accelerator repository, five projects and a full capstone engagement to show employers.
How the FDE PRO program works
Eight stations take you from a customer’s problem to measurable business value. Every project in the program travels the full route.
Understand
Discovery with stakeholders: the real problem and how success is measured.
Design
Architecture, data flow, security model and trade-offs.
Build
Production Python, APIs, RAG and agents.
Integrate
Enterprise systems, MCP tools and the customer’s identity.
Deploy
Containers, Kubernetes, Terraform and CI/CD.
Observe
Traces, metrics, evaluation scores and cost per request.
Improve
Fix, tune, harden and cut cost.
Deliver value
ROI, executive demo and a clean handover.
Twin-track learning: engineering plus customer engagement
Each week runs on two tracks. The engineering track builds technical depth; the Customer Engagement Lab makes you use it the way an FDE does, in front of a “customer”.
Engineering track
Four sessions a week
Python, APIs, cloud, RAG, agents, MCP, Kubernetes, Terraform, security and observability, taught hands-on with a lab in every session.
Customer Engagement Lab
One session a week
You apply that week’s technology to a simulated customer situation: discovery calls, workflow maps, design documents, security reviews, incident updates, SOWs and executive demos.
Six questions every project must answer
- What customer problem does this solve?
- How would you deploy it inside a real enterprise?
- How do you prove it works?
- What is it allowed to do?
- How will you secure, monitor and operate it?
- What measurable business value did the customer get?
Your FDE Accelerator
Every lab adds a reusable component to your own FDE Accelerator repository. By week 12 you own a delivery kit: discovery templates, service and agent templates, an MCP server, a Terraform module, a CI/CD pipeline, an evaluation harness, dashboards, runbooks and an ROI calculator. It is how real FDE teams work: reuse what is proven, then adapt it to each customer.
FDE course curriculum: the 12-week route
Three months, three phases. Every week has four engineering sessions and one Customer Engagement Lab, a hands-on lab in every session and a new component for your FDE Accelerator. Open any week to see its topics, labs and deliverables.
- Sessions 1 to 4
- Engineering track: concept, live build, hands-on lab
- Session 5
- Customer Engagement Lab: apply the week in a simulated customer situation
- Practice
- 8–10 hours of guided lab practice outside class
Month 1: Build Weeks 1–4
Engineering & cloud foundation
Write production software and ship it to the cloud.
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Week 1
The FDE mindset and your developer toolkit
Understand what FDEs do all day, set up a professional workstation and make your first LLM call.
Topics, labs and deliverables
- The FDE role: how it differs from software, AI, DevOps and solutions engineers, architects and consultants; pre-sales vs post-sales; POC, pilot, MVP and production
- Enterprise vocabulary: stakeholders, champion, decision maker, procurement, security review, architecture review board, UAT, SOW, SLA and SLO, success criteria
- Linux for FDEs: filesystem, processes, services and systemd, ports, DNS, SSH, permissions, environment variables and logs; grep, awk, sed, curl and jq
- Git and GitHub: branching, pull requests, merge vs rebase, tags and releases, keeping secrets out of repositories
- Your first LLM call: call a model API from Python and get structured JSON back on day one
Hands-on lab
Set up the FDE workstation (VS Code, Python virtual environments, Git, CLI tools) and publish a first-LLM-call repository with a README a customer could follow.
Customer Engagement Lab
Decode a vague request: turn “we want AI for support” into a problem statement, a stakeholder map and success criteria.
Adds to your FDE Accelerator: Repository skeleton, README and discovery templates
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Week 2
Production Python
Write Python that survives real customer environments: typed, tested, logged and resilient.
Topics, labs and deliverables
- Core to advanced: functions, OOP, modules and packages, exceptions, JSON, YAML and CSV processing, regular expressions
- Modern Python: type hints, dataclasses, decorators, generators, iterators and context managers
- Concurrency: async Python and HTTP clients; threads, processes and queues
- Production patterns: structured logging, configuration and .env files, retries with backoff, timeouts, circuit breakers, idempotency
- Testing and packaging: pytest, fixtures, mocking external APIs, dependency management
Hands-on lab
Build an enterprise API connector: a paginated, rate-limit-aware REST client with retries, tests and a command-line interface.
Customer Engagement Lab
Run a mock discovery call with the Why, What, Who, Where, When and How question bank, then write up the notes.
Adds to your FDE Accelerator: Resilient API-client library with retries, logging and config
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Week 3
APIs, data and SQL
Expose clean, secure APIs over messy enterprise data.
Topics, labs and deliverables
- FastAPI: routing, Pydantic models, dependency injection, middleware, background tasks, async endpoints, OpenAPI and Swagger, versioning
- API engineering: API keys and JWT, pagination, rate limiting, caching, idempotency keys, webhooks; REST vs GraphQL vs SOAP
- SQL FDEs use daily: joins, GROUP BY, CTEs, window functions, indexes, transactions and reading query plans
- Databases: PostgreSQL and Redis hands-on; MongoDB overview; systems of record, OLTP vs OLAP, warehouse, lake and lakehouse
- Data reality: duplicates, missing values, schema mismatch, PII, freshness and data contracts; pipeline basics with Kafka and Airflow concepts
Hands-on lab
Customer onboarding service: FastAPI, PostgreSQL and Redis with JWT auth, webhooks and an automated data-quality report.
Customer Engagement Lab
Map a business process (support-ticket handling) into a technical workflow diagram the customer can validate.
Adds to your FDE Accelerator: FastAPI service template with data-quality checks
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Week 4
Cloud and containers: AWS first, Azure and Google Cloud ready
Package your service and deploy it securely on AWS, with working knowledge of Azure and Google Cloud.
Topics, labs and deliverables
- AWS core: IAM roles and policies, VPC, subnets, security groups, EC2, S3, RDS, Lambda, API Gateway, CloudWatch, CloudTrail, Secrets Manager, KMS
- Azure essentials: resource groups, VNets and NSGs, Functions, Storage, Key Vault, Azure Monitor
- Google Cloud essentials: projects, IAM, VPC, Cloud Run, Cloud Storage, Secret Manager
- Docker: images, Dockerfiles, multi-stage builds, volumes, networks, Compose, image scanning and registries (ECR, ACR, Artifact Registry)
- Cost guardrails: budgets and alerts so your labs never produce a surprise bill
Hands-on lab
Ship it: Python service to Docker image to Amazon ECR to ECS Fargate behind a load balancer, with secrets in Secrets Manager.
Customer Engagement Lab
Write a one-page high-level design (HLD) with an architecture diagram and a network diagram.
Adds to your FDE Accelerator: Dockerfile, AWS deployment template and HLD template
- Project 1 delivered: Customer Integration Service
Month 2: Integrate Weeks 5–8
Enterprise AI engineering
Make AI useful inside real enterprise systems.
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Week 5
GenAI and LLM engineering
Use LLMs like an engineer: predictable outputs, the right model for the job and a cost you can explain.
Topics, labs and deliverables
- How LLMs work for engineers: tokens, context windows, embeddings, attention (intuition), inference, temperature and top-p
- Prompt and context engineering: zero- and few-shot prompts, role prompts, structured JSON output, templates, chaining and context management
- Models across clouds: Amazon Bedrock (Converse API), Azure OpenAI in Microsoft Foundry, Gemini on Google Cloud and open-weight models run locally
- Model selection: quality, cost, latency, context length, tool calling and privacy; when a small model wins; prompting vs RAG vs fine-tuning (LoRA, PEFT) vs distillation
- Token economics: input vs output tokens, caching, batching and streaming
Hands-on lab
Build a multi-provider AI gateway: one API, swappable models, with tokens, latency and cost logged for every request.
Customer Engagement Lab
Write a model-selection memo for a customer with data-residency constraints.
Adds to your FDE Accelerator: Multi-model AI gateway
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Week 6
RAG engineering and evaluation
Build retrieval-augmented generation that answers with citations, and prove its quality with numbers.
Topics, labs and deliverables
- RAG pipeline: parsing, chunking (fixed, recursive, semantic, parent-child), embeddings and vector stores: FAISS, Chroma, pgvector, OpenSearch
- Retrieval quality: hybrid dense and keyword search, metadata filters, query rewriting, multi-query retrieval, reranking, context compression
- Enterprise answers: citations and permission-aware retrieval, so users only see what they are allowed to see; managed RAG with Bedrock Knowledge Bases and Azure AI Search
- Evaluation with Ragas: faithfulness, answer relevancy, context precision and recall; golden datasets; regression tests in CI
- What comes next: Graph RAG and agentic RAG
Hands-on lab
Enterprise Knowledge Assistant: ingest policy documents, answer with citations and score the system with Ragas.
Customer Engagement Lab
Agree the acceptance criteria: an evaluation plan the customer signs before you build.
Adds to your FDE Accelerator: RAG module and evaluation harness
- Project 2 delivered: Enterprise Knowledge Assistant
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Week 7
Agentic AI: tools, LangGraph and multi-agent systems
Design agents that act, and that stop, ask and stay inside their permissions.
Topics, labs and deliverables
- Agent fundamentals: workflows vs agents; the reason, act, observe loop; function and tool calling; structured tools
- LangChain and LangGraph: models, prompts, tools and retrievers; state, nodes, edges, conditional routing, loops, checkpoints, threads, persistence and human-in-the-loop
- Multi-agent patterns: router, supervisor, hierarchical and parallel specialists
- Managed agent platforms: Amazon Bedrock Agents and AgentCore, Microsoft Foundry Agent Service, Google Agent Development Kit (ADK)
- Agent guardrails: step limits, budgets, tool allow-lists and approval gates
Hands-on lab
IT-Ops multi-agent system: a supervisor routes to CloudWatch, Kubernetes and Jira agents and waits for human approval before any change.
Customer Engagement Lab
Write the agent permission spec: what it may read, what it may change and who approves.
Adds to your FDE Accelerator: LangGraph agent template with an approval step
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Week 8
MCP, enterprise integration and identity
Connect AI to the systems enterprises actually run, under the customer’s identity and access rules.
Topics, labs and deliverables
- Model Context Protocol (MCP): hosts, clients and servers; tools, resources and prompts; local (stdio) and remote (HTTP) servers; authentication and authorization
- Build MCP servers: for a database, GitHub and ServiceNow; tool governance, RBAC and audit trails
- Enterprise integrations: ServiceNow, Jira, GitHub, Slack, Microsoft Teams, Salesforce and SharePoint; webhooks and legacy SOAP; API limits, schema mapping, private endpoints, proxies and firewalls
- Identity for AI: OAuth 2.0, OIDC, SAML, SSO, SCIM and MFA; Microsoft Entra ID; service principals, managed and workload identities; acting on behalf of a user
Hands-on lab
ServiceNow AI agent: an MCP server that searches knowledge and creates and updates incidents with SSO-aware permissions.
Customer Engagement Lab
Answer the customer security team’s questionnaire on data flow, identity and access.
Adds to your FDE Accelerator: MCP server template with OAuth and OIDC
- Project 3 delivered: ServiceNow AI Agent via MCP
Month 3: Deploy & deliver Weeks 9–12
Production & customer engineering
Run it in production and prove the business value.
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Week 9
Kubernetes, Terraform and CI/CD
Turn a working prototype into a repeatable, automated production deployment.
Topics, labs and deliverables
- Kubernetes: pods, deployments, services, ingress, ConfigMaps, Secrets, namespaces, RBAC, probes, requests and limits, HPA; rolling, blue/green and canary releases
- Managed Kubernetes: EKS with AKS and GKE equivalents, private clusters, workload identity and Helm charts
- Terraform: providers, resources, data sources, variables, outputs, modules, remote state and locking, import, drift, plan and apply
- CI/CD: GitHub Actions with OIDC to AWS (no long-lived keys): test, scan, build, push, deploy, smoke test; environment promotion, approval gates and rollback
- GitOps: Argo CD, plus Jenkins and Azure DevOps equivalents
Hands-on lab
Ship the IT-Ops agent to EKS: Terraform for VPC, EKS and IAM, a Helm chart and a GitHub Actions pipeline using OIDC.
Customer Engagement Lab
Write the POC-to-production plan: everything that changes between the demo and go-live.
Adds to your FDE Accelerator: Terraform module, Helm chart and CI/CD pipeline
- Project 4 delivered: IT-Ops Multi-Agent Platform
- Capstone sprint 0: discovery and architecture
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Week 10
AI security, governance and enterprise networking
Pass the customer’s security review: attack your own system, then close every gap.
Topics, labs and deliverables
- LLM threats: OWASP Top 10 for LLM Applications: direct and indirect prompt injection, sensitive information disclosure, insecure output handling, excessive agency; tool and retrieval poisoning
- Defenses: guardrails, input and output filtering, least-privilege tools, human approval, sandboxing, secrets management and audit logs
- Red teaming: jailbreaks, data exfiltration and unauthorized tool use against your own agent, then the fixes
- Responsible AI and governance: PII masking, data residency, auditability, human-in-the-loop and access governance
- Enterprise networking: DNS, TLS, proxies, NAT and firewalls; VPC and VNet design, private subnets, PrivateLink and Private Endpoints, VPN; no-inbound, proxy-only and air-gapped environments
Hands-on lab
Secure Banking AI Assistant: PII masking, RBAC-filtered retrieval, audit trail and approvals, plus a red-team report of attacks and fixes.
Customer Engagement Lab
Present your security controls to a simulated CISO and architecture review board.
Adds to your FDE Accelerator: Guardrails, red-team test suite and security-review template
- Project 5 delivered: Secure Banking AI Assistant
- Capstone sprint 1: working POC
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Week 11
Observability, cost and troubleshooting
Know what your AI system is doing, what it costs and how to fix it at 2 a.m.
Topics, labs and deliverables
- Observability: logs, metrics, traces, alerts and dashboards with CloudWatch, OpenTelemetry, Prometheus and Grafana
- AI observability: token usage, model, retrieval and tool latency, agent trajectories, tool failures, evaluation scores and cost per request, with LangSmith- and Langfuse-style tracing
- Cost and performance: model routing, prompt and semantic caching, batching, streaming and async processing; the cut-cost-by-60% challenge; the production-readiness gate
- Troubleshooting: IAM AccessDenied, DNS failures, timeouts, 403, 429 and 5xx; CrashLoopBackOff, OOMKilled, ImagePullBackOff; hallucinations, bad retrieval, wrong tool, agent loops, context overflow
- Incident response: triage, impact, logs, metrics and traces, root cause, mitigation, customer communication and RCA
Hands-on lab
Break-fix gauntlet: ten production failures injected into your deployed stack. Find them, fix them and write the RCA.
Customer Engagement Lab
Write the customer update and the RCA for a P1 outage.
Adds to your FDE Accelerator: Observability dashboards, runbook and RCA template
- Capstone sprint 2: deployed, monitored and evaluated
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Week 12
Customer engineering and capstone delivery
Deliver like an FDE: architecture, proposal, ROI and an executive demo.
Topics, labs and deliverables
- Solution architecture: functional and non-functional requirements, HLD and LLD, sequence and data-flow diagrams, trade-offs
- Proposals and SOWs: scope, out of scope, dependencies, risks, timeline, acceptance criteria and success metrics
- AI ROI: hours saved, cost avoided, run cost and payback period
- Demo engineering: the 5-minute executive demo, the 20-minute technical demo and the 45-minute engineering workshop
- Multi-cloud and hybrid: an AI gateway across AWS, Azure and Google Cloud; self-hosted open-weight models for restricted environments
- The FDE interview loop: coding, system design, customer-scenario and behavioral rounds; portfolio and resume
Hands-on lab
Capstone sprint 3: UAT with the customer, go-live rehearsal, hypercare drill and executive presentation.
Customer Engagement Lab
Present business value, architecture and ROI to a mock CXO panel.
Adds to your FDE Accelerator: SOW template, ROI calculator and executive demo script
- Capstone delivered: the GlobalBank engagement
Enterprise projects you will build
Five projects, each built from a realistic customer brief and each answering the six FDE questions. They build on one another, so by week 10 you have a connected portfolio, not five toy demos.
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P1 Weeks 2–4
Customer Integration Service
A customer’s CRM and support data sits behind different APIs with different schemas. Pull it, clean it and serve it through one reliable API.
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P2 Weeks 5–6
Enterprise Knowledge Assistant
Employees lose hours hunting through HR and IT policies. Build an assistant that answers with citations, only from documents each user is allowed to see.
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P3 Week 8
ServiceNow AI Agent via MCP
The service desk wants an agent that searches knowledge and opens and updates incidents, without ever holding more access than the person using it.
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P4 Weeks 7 and 9
IT-Ops Multi-Agent Platform
On-call engineers drown in alerts. A supervisor agent investigates with specialist agents, proposes a fix and waits for human approval.
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P5 Week 10
Secure Banking AI Assistant
A bank wants AI help for transaction investigations under strict PII, audit and approval rules.
Every project ships like a customer deliverable: a README a customer can follow, an architecture diagram, tests or an evaluation report, a deployment you can demo and a one-page summary that answers the six FDE questions.
Capstone: the GlobalBank FDE engagement
“We want an AI system that reduces IT support workload by 40%.”
You don’t start by coding. You start by asking questions.
Squads of three or four work like a real FDE pod, with trainers playing GlobalBank’s stakeholders: the IT head, the security lead, the service-desk manager and the CFO.
Sprint 0, Week 9
Discover and design
Stakeholder interviews, requirements, success criteria and the high-level design.
Sprint 1, Week 10
Build the POC
Knowledge assistant, ServiceNow tools via MCP, the agent workflow, identity and authorization.
Sprint 2, Week 11
Harden and deploy
Terraform and CI/CD to the cloud, observability, evaluation harness, cost model and security review.
Sprint 3, Week 12
Prove and hand over
UAT, go-live rehearsal, a hypercare drill and the executive presentation with ROI.
What you hand over
- Discovery notes
- Requirements document
- HLD and LLD
- Working, deployed system
- Evaluation report
- Security review
- Runbook
- SOW
- ROI model
- Executive deck
A production trace you will learn to read
Request ID FDE-10291 LLM latency 820 ms Retrieval 180 ms Tool call 240 ms Tokens 2,840 Cost $0.012 Faithfulness 0.94 Context recall 0.91
How an FDE proves value: an illustrative ROI
- Today
- 100 employees spend 30 minutes a day on a task
- With the AI system
- the same task takes 10 minutes
- Time saved
- 20 min × 100 people ≈ 33 hours a day, about 8,300 hours a year (250 working days)
- Value
- at ₹500 an hour, roughly ₹41 lakh a year, before subtracting run cost
- Payback
- build cost ÷ monthly net savings
The FDE’s job is to answer one question with numbers: why should the customer deploy this?
Tools and technologies you will use
- Languages
- Python, SQL, Bash
- Backend and data
- FastAPI, Pydantic, PostgreSQL, Redis, pgvector; Kafka and Airflow concepts
- GenAI and RAG
- Amazon Bedrock, Azure OpenAI, Gemini, open-weight models, embeddings, FAISS, Chroma, OpenSearch, rerankers
- Agents
- LangChain, LangGraph, Model Context Protocol (MCP), Bedrock AgentCore, Microsoft Foundry Agent Service, Google ADK
- Evaluation and observability
- Ragas, LangSmith, Langfuse, OpenTelemetry, CloudWatch, Prometheus, Grafana
- Cloud
- AWS (primary), Microsoft Azure, Google Cloud
- DevOps
- Git, GitHub Actions, Docker, Kubernetes (EKS), Helm, Terraform, Argo CD
- Enterprise systems
- ServiceNow, Jira, GitHub, Slack, Microsoft Teams, Microsoft Entra ID, Salesforce, SharePoint
By the end of the program, you can
- Run a discovery call and turn a vague request into requirements and success criteria
- Map business workflows to architecture and write HLDs and LLDs
- Write production Python and FastAPI services
- Build RAG systems with citations, and measure their quality
- Build tool-calling agents and multi-agent systems with LangGraph
- Build MCP servers and integrate ServiceNow, Jira, GitHub and Teams
- Implement SSO, OAuth/OIDC and least-privilege access for AI agents
- Deploy on AWS with Docker, Kubernetes, Terraform and CI/CD, and find your way around Azure and Google Cloud
- Secure, red-team, monitor and cost-optimize AI applications
- Troubleshoot production incidents and write RCAs
- Write SOWs, calculate AI ROI and demo to executives
FDE careers and placement support
FDE PRO prepares you for roles where companies need engineers who can build AI and make it work inside real organizations.
Roles you can target
- Forward Deployed Engineer (AI)
- Applied AI Engineer
- AI Solutions Engineer / Customer Engineer
- Agentic AI Engineer
- AI Integration Engineer
- GenAI Engineer
- Technical Implementation / Deployment Engineer
- Associate Solutions Architect (AI)
Where FDEs work
- AI product companies and model providers deploying their platforms into large enterprises
- Enterprise software platforms rolling out AI agents inside their customers’ systems
- Global consulting and IT services firms building AI delivery practices
- Global Capability Centres (GCCs) in Hyderabad and Bengaluru running internal AI programs
- AI startups building solutions for banking, healthcare, retail and manufacturing
The FDE interview loop, and how we prepare you
- Coding
- Practical Python: parse, transform, call APIs, handle failure.
- System design
- Design an AI system with RAG, agents, identity and deployment.
- Customer scenario
- Run discovery with a ‘customer’ who is vague, busy or sceptical.
- Behavioral
- Ownership, ambiguity and delivering under pressure.
Placement support
- ATS-friendly resume and LinkedIn profile positioned for FDE and applied AI roles
- Portfolio review: your FDE Accelerator repository, five projects and the capstone
- Mock interviews covering coding, system design, customer scenarios and behavioral rounds
- Placement assistance from the team behind 5,500+ placed alumni
Your portfolio on day one of job hunting: a public FDE Accelerator repository, five deployed projects and a capstone with discovery notes, design documents, an evaluation report, a security review, an SOW and an executive deck. That is the evidence FDE hiring managers ask for.
FDE course fee, batches and format
Course fee
₹30,000
Complete 3-month program
What’s included
- 120+ hours of live, instructor-led training
- 60+ hands-on labs, one in every session
- 5 enterprise projects and the GlobalBank capstone
- Your own FDE Accelerator repository
- Cloud Soft lab guides and handbooks
- Resume, portfolio and interview preparation
- Placement assistance
- Duration
- 3 months (12 weeks), 120+ hours of live training
- Sessions
- 5 sessions a week, 2 hours each
- Format
- Classroom at Ameerpet, beside Ameerpet Metro Station, or live online
- Next batch
- Call or WhatsApp +91 96660 19191 for start dates and timings
Who should join
- Freshers (B.Tech, MCA, M.Sc., BCA) with programming basics who want a premium AI career track
- Software developers in Python, Java, .NET or Node.js moving into applied AI
- DevOps, cloud and SRE engineers adding GenAI and agent skills
- Support, implementation and integration engineers ready to step up to customer engineering
- Data engineers and analysts who want to ship AI solutions
- Pre-sales and solutions engineers who want hands-on build depth
- NRIs and overseas learners through live online batches
Readiness checklist
- You can write basic programs in any language: variables, loops and functions
- You are comfortable on the command line, or willing to practise it in week 1
- A laptop that runs Docker smoothly (16 GB RAM recommended, 8 GB works for most labs)
- Free accounts: GitHub, AWS free tier, an Azure or Google Cloud trial and a ServiceNow developer instance
- 8–10 hours a week of practice outside class, because this is a fast-track program
How to join
Book a free demo
Call or WhatsApp +91 96660 19191 for batch dates, timings and a demo slot.
Choose your format
Classroom at Ameerpet or live online, and the batch timing that suits you.
Start week 1
Set up your FDE workstation and make your first LLM call in the first week.
Why learn FDE with Cloud Soft Solutions
- Led by a practitioner
- The program is led by Sreekanth Bathalapalli (Cloud), Managing Partner & Lead Trainer, with 20 years in IT across cloud and DevOps architecture and AI/GenAI engineering.
- 5,500+ alumni placed
- Our training and placement record across programs, now applied to the FDE role.
- A production content library
- The same lab guides and handbooks behind our APEX and NEXUS programs: Amazon Bedrock, LangChain, FAISS, RAG pipelines, EKS, Helm, Docker, Jenkins and more.
- 70% hands-on
- Every session ends with something running, not just slides.
- Classroom or online
- Learn beside Ameerpet Metro Station in Hyderabad, or join live online from anywhere.
Looking for a broader path? Compare FDE PRO with APEX, our AI, ML, cloud and cyber security engineering program, or see all Cloud Soft Solutions programs, including NEXUS for cloud, DevOps, SRE and AIOps.
Forward Deployed Engineer course: frequently asked questions
What is a Forward Deployed Engineer (FDE)?
A Forward Deployed Engineer (FDE) is a software engineer who works directly with customers to turn their business problems into working, production-grade solutions. In AI, an FDE runs discovery, designs the architecture, builds with LLMs, RAG and agents, integrates enterprise systems, deploys to the cloud and proves measurable business value.
Is Forward Deployed Engineering a good career in India?
Yes, for engineers who enjoy both building and solving customer problems. As companies move AI from pilots into production, they need engineers who can integrate it with real systems, secure it and prove its value. AI companies, enterprise software platforms and global consulting firms now post FDE roles in India, including in Hyderabad.
What is the fee for the FDE course at Cloud Soft Solutions?
The FDE PRO course fee is ₹30,000 for the complete 3-month program. It covers live training, hands-on labs, five enterprise projects, the GlobalBank capstone, lab guides and placement support.
How long is the course and how are classes scheduled?
Three months (12 weeks), with five 2-hour sessions a week, for 120+ hours of live training. Four sessions each week are engineering; the fifth is a Customer Engagement Lab. Call +91 96660 19191 for the next batch dates and timings.
Can freshers join the FDE course?
Yes, if you can already write basic programs in any language. Python is taught from fundamentals to production standard in the first weeks. Because this is a fast track, plan for 8–10 hours of practice a week outside class.
Do I need prior AI or machine learning experience?
No. LLM concepts, prompt and context engineering, RAG, agents and evaluation are all taught in the program. Comfort with programming and a willingness to work with real systems matter more.
How is an FDE different from a Solutions Engineer or Solutions Architect?
A solutions engineer mostly demos the product and helps win the deal; a solutions architect designs the target architecture. An FDE does both, then builds, integrates, deploys and supports the solution in the customer’s environment until it delivers results in production.
How is FDE PRO different from a regular GenAI or Agentic AI course?
Most AI courses stop at a working demo. FDE PRO is built around the customer engagement lifecycle: discovery, architecture, integration, identity, deployment, security, evaluation, ROI and executive communication. You learn to take AI all the way to production.
Which cloud platforms are covered?
AWS is the primary hands-on platform, including IAM, VPC, ECS, EKS, Lambda and Amazon Bedrock. You also work with Microsoft Azure (Azure OpenAI, Microsoft Foundry, Key Vault, Entra ID) and Google Cloud (Gemini, Cloud Run, Agent Development Kit).
Will I learn MCP, LangGraph and multi-agent systems?
Yes. You build MCP servers for a database, GitHub and ServiceNow, design LangGraph agents with checkpoints and human approval, and build a supervisor-based multi-agent system for IT operations.
What projects will I build?
Five enterprise projects: a Customer Integration Service, an Enterprise Knowledge Assistant, a ServiceNow AI Agent via MCP, an IT-Ops Multi-Agent Platform and a Secure Banking AI Assistant. Then the GlobalBank capstone, a simulated end-to-end customer engagement.
Is the course available online?
Yes. Attend in the classroom at Ameerpet, beside Ameerpet Metro Station, or join live online from anywhere, including from outside India.
What jobs can I apply for after the course?
Forward Deployed Engineer, Applied AI Engineer, AI Solutions or Customer Engineer, Agentic AI Engineer, AI Integration Engineer, GenAI Engineer, Technical Implementation Engineer and Associate Solutions Architect (AI) roles.
Do you provide placement support?
Yes. You get an ATS-friendly resume and LinkedIn profile, a portfolio review, mock interviews covering coding, system design and customer scenarios, and placement assistance from the team that has placed 5,500+ alumni.
Will I have to pay for cloud or AI API usage?
Labs are designed around free tiers, local tools and low-cost models, and you set up budgets and alerts in week 4. Some exercises on your own cloud or API accounts may incur small usage charges; the labs show you how to keep them low.
What laptop do I need?
Any laptop that runs Docker smoothly. 16 GB RAM is recommended, especially for running local models; 8 GB works for most labs.
How is FDE PRO different from APEX and NEXUS?
APEX is our broad AI, ML, cloud and cyber security engineering program, and NEXUS focuses on cloud, DevOps, SRE and AIOps. FDE PRO is a focused 3-month fast track for one job: taking AI solutions from a customer’s problem to production.
Where is Cloud Soft Solutions located?
513, 5th Floor, Aditya Enclave, Nilagiri Block, beside Ameerpet Metro Station, Ameerpet, Hyderabad, Telangana 500016. Call +91 96660 19191 or +91 99496 16388.
How do I book a free demo?
Call or WhatsApp +91 96660 19191, call +91 99496 16388 or email info@cloudsoftsol.com. We will share the next batch date, timings and a demo slot.
Book your free FDE PRO demo
Meet the trainer, see the labs and get the next batch dates. Classroom in Ameerpet or live online.
Cloud Soft Solutions513, 5th Floor, Aditya Enclave, Nilagiri Block
Beside Ameerpet Metro Station, Ameerpet
Hyderabad, Telangana 500016
info@cloudsoftsol.com
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