AI Forward Deployed Engineer Training Course in Durgapur (Live Online) by Uncodemy

• Role of a Forward Deployed Engineer in enterprise AI delivery
• How FDE differs from AI/ML engineering and solutions architecture
• Enterprise AI lifecycle: discovery to production
• Common enterprise AI use cases and failure modes
• Client-facing delivery models and engagement structures
• Overview of the modern AI stack: LLMs, RAG, agents, MCP

• Python fundamentals for AI engineering
• Working with libraries and virtual environments
• SQL for querying and joining enterprise datasets
• Git version control and collaborative workflows
• REST API design, request/response handling
• FastAPI basics and building API endpoints
• Authentication, error handling and API best practices

• How large language models work at a conceptual level
• Tokens, embeddings and context windows
• OpenAI APIs and Hugging Face model ecosystem
• Generative AI application patterns
• Model selection trade-offs for enterprise use
• Cost, latency and quality considerations

• Prompt design principles for reliable outputs
• System prompts, few-shot prompting and role prompting
• Context engineering for long and multi-turn interactions
• Structured output and function calling prompts
• Prompt evaluation and iteration
• Prompt security and injection risks

• Enterprise data sources: databases, documents, APIs
• Data ingestion and preprocessing pipelines
• Document parsing, chunking and metadata
• Embedding generation pipelines
• Data freshness and incremental updates
• Data governance and access control in AI pipelines

• What RAG is and why enterprises use it
• Vector databases and PostgreSQL/pgvector
• Retrieval strategies: dense, sparse and hybrid
• Reranking and relevance scoring
• Building an end-to-end RAG pipeline
• Evaluating RAG quality with RAGAS
• Common RAG failure modes and fixes

• Agent architectures and reasoning loops
• Tool calling and function schemas
• LangChain and LangGraph for agent orchestration
• LlamaIndex for knowledge agents
• MCP (Model Context Protocol) fundamentals
• Multi-step and multi-tool agent workflows
• Guardrails and failure handling for agents

• Building AI-enabled applications with FastAPI
• Integrating LLMs, RAG and agents into apps
• Backend services and async processing
• UI integration patterns for AI apps
• Managing state, sessions and conversation history
• Deploying AI app services to production

• Why AI evaluation differs from traditional software testing
• Evaluation metrics for LLM outputs (RAGAS, custom metrics)
• LangSmith and Langfuse for tracing and observability
• Monitoring production AI systems
• Handling drift, regressions and failures
• Continuous evaluation and feedback loops

• Threat models for enterprise AI systems
• Prompt injection and data exfiltration risks
• Output moderation and safety filters
• Data privacy and PII handling in AI pipelines
• Access control and tenant isolation
• Compliance considerations for enterprise deployments

• Containerising AI services with Docker
• AWS and Azure fundamentals for AI deployment
• CI/CD pipelines for AI applications
• Secrets and environment configuration
• Scaling and performance tuning
• Rollback strategies and production readiness

• Running client discovery and scoping sessions
• Translating business problems into AI solution designs
• Solution architecture for RAG and agent systems
• Estimating effort, cost and risk
• Presenting AI solutions to enterprise stakeholders
• Consulting communication and documentation

• End-to-end enterprise AI deployment project
• RAG pipeline + AI agent integration in a client-style scenario
• Deployment on cloud with CI/CD
• Evaluation, observability and guardrails applied to the capstone
• Capstone presentation and defence
• Portfolio-ready deliverables

Durgapur's professional landscape has long been shaped by heavy industry — steel, cement, mining equipment and power — and its technical talent often turns toward Kolkata, Bengaluru or remote-first employers for applied AI roles. As companies in manufacturing, logistics and services across the Durgapur-Asansol industrial belt begin exploring AI-driven workflows, demand is emerging for engineers who can move AI from a pilot into a production system inside a client organisation. The city's engineering colleges, expanding services sector and proximity to Kolkata make it a realistic base for candidates pursuing Forward Deployed Engineer careers — whether locally, in nearby metros, or through remote roles with national employers.

Interview panels typically weigh three things first. One, whether you can design and defend a RAG or agent system architecture suited to a business problem. Two, whether you have genuine deployment experience — Docker, CI/CD, cloud, monitoring, evaluation. Three, whether you can handle a client-facing conversation: scoping, translating business requirements, presenting solutions. Candidates who can walk an interviewer through a completed enterprise AI deployment, rather than just a model notebook, stand out fastest.

This program is built to develop exactly those three capabilities through live sessions, hands-on projects, cloud deployment work and a capstone that mirrors real client engagements.

Since the curriculum covers enterprise AI fundamentals, Python/SQL/Git/API engineering, LLM applications, prompt and context engineering, RAG, agents and MCP, evaluation, security, cloud deployment and customer discovery, graduates can pursue several client-facing AI delivery roles rather than a single title. The table below maps each role to the core tools most relevant to it.

Over a 2–5 year progression, professionals usually move from entry-level Forward Deployed Engineer or AI Solutions Engineer positions into senior FDE roles and eventually AI Solutions Architect roles, taking on larger client engagements, deeper architecture ownership and team leadership. The skills built here — RAG, agents, MCP, evaluation, deployment and customer discovery — form the foundation for that trajectory.

Salaries for Forward Deployed Engineer and adjacent AI delivery roles in Durgapur and the surrounding region differ by employer, experience and technical scope. Entry-level AI Forward Deployed Engineers typically start at ₹6–10 LPA, while senior FDEs and AI Solutions Architects can earn significantly more. The ranges below are indicative market ranges Uncodemy uses across its course pages.

Current India reporting places Forward Deployed Engineer (FDE) salaries broadly around ₹9–27 LPA, although actual compensation varies substantially by experience, company and technical scope (source: Express Computer).

Sources reviewed: AmbitionBox, Glassdoor, Indeed, Express Computer and Uncodemy placement records. Reviewed: October 2026. These ranges are compiled by comparing publicly reported salary data against internal placement information, and they are reviewed periodically so they stay broadly aligned with the market. Actual offers in Durgapur and nearby markets depend on employer size and sector, interview performance, prior experience and the specific responsibilities of the role, so treat the table as a planning guide.

Demand for Forward Deployed Engineer skills reaches Durgapur learners through several industries, and Uncodemy's 850+ hiring partners span many of them. The sectors below are where AI deployment skills are most commonly applied for professionals based in and around Durgapur:

• SaaS and product organisations: Companies adding AI features and needing engineers to roll them out to enterprise customers.
• IT services: Firms delivering RAG and agent solutions for global clients, including teams based in Kolkata and NCR.
• Fintech: Payments, lending and wealth-tech players applying AI agents and RAG in customer-facing and internal workflows.
• Product engineering: Captive centres and GCCs building AI-enabled products that must operate in production at scale.
• Consulting & system integrators: Advisory and SI firms running enterprise AI programmes for large corporate clients.

Because each of these sectors builds on the same foundation — RAG, agents, evaluation, deployment and client-facing delivery — a broad Forward Deployed Engineer program equips you to work across industries rather than committing to one early in your career.

Becoming an AI Forward Deployed Engineer is a staged process where each capability builds on the previous one. The seven milestones below mirror this program's order, so you always know what comes next and why it matters.

1. FDE & Enterprise AI Fundamentals: Grasp what Forward Deployed Engineers handle, how enterprise AI delivery is organised, and where this role fits.
2. Python, SQL, Git & API Engineering: Establish the engineering foundation — Python, SQL, Git workflows and REST API development using FastAPI.
3. LLM Fundamentals & Generative AI: Understand how LLMs operate, how to work with OpenAI APIs and Hugging Face models, and how to design generative AI applications.
4. Prompt & Context Engineering: Learn prompt design, structured output, function calling and context handling for dependable results.
5. RAG & Enterprise Knowledge Systems: Create end-to-end RAG pipelines with vector databases, retrieval, reranking and RAGAS evaluation.
6. AI Agents, Tool Calling & MCP: Design agent architectures, tool calling schemas, LangChain and LangGraph workflows, and MCP integrations.
7. Cloud Deployment, Evaluation & Capstone: Deploy AI systems on cloud using Docker and CI/CD, add evaluation and observability, and complete the enterprise AI capstone.

Timeline: 5-6 months, covering all seven steps from fundamentals through the capstone.

Before choosing an AI course, consider the factors that shape your result: duration, total learning hours, live teaching, tool coverage, project depth and career support. The table below lists these factors for this program so you can compare them with any alternative.

In contrast to Uncodemy's LLM Engineering or RAG Engineering courses, this AI Forward Deployed Engineer program takes the broader, client-facing enterprise-deployment path — covering discovery, RAG, agents, MCP, evaluation, deployment and consulting. LLM Engineering and RAG Engineering narrow in on deeper technical depth within their respective domains. If your aim is a client-facing enterprise AI delivery career, this program offers the stronger fit. If you already have solid LLM or RAG depth and want to go further in one specific area, a narrower specialisation may suit you better. The capstone project and client-scenario interview preparation further set this program apart from narrower alternatives.

Since Uncodemy does not operate a classroom centre in Durgapur, learners from the city attend this AI Forward Deployed Engineer program entirely live online. That does not make it a diluted version. You are part of the same batches, taught by the same trainers and covering the same 13-module curriculum as students in Uncodemy's classroom programs. Classes run in real time, so you can pose questions, share your screen for code review and discuss RAG or agent architecture with the trainer as the session proceeds.

Learning online also spares you the need to commute or relocate to a metro for training — a real advantage for working professionals in Durgapur. You can choose a weekday or weekend batch that suits your job and revise using session recordings.

If a physical classroom is preferable and you are able to travel, in-person training remains available at Uncodemy's Delhi and Noida centres. Fees and curriculum are the same in both modes.

The program is 5–6 months long. It covers 177 verified learning hours, more than 100 live sessions and 13 modules.

Basic programming familiarity is helpful, but deep prior coding experience is not mandatory. Module 2 teaches Python, SQL, Git and API engineering from the ground up.

You will work with 20+ tools: Python, SQL, Git, GitHub, REST APIs, FastAPI, OpenAI APIs, Hugging Face, LangChain, LangGraph, LlamaIndex, MCP, RAG, Vector Databases, PostgreSQL/pgvector, Docker, AWS/Azure, CI/CD, LangSmith/Langfuse and RAGAS.

Yes. Module 6 covers RAG & Enterprise Knowledge Systems (15 hours) and Module 7 covers AI Agents, Tool Calling & MCP (15 hours). Both are hands-on modules with project work.

The capstone is Module 13, Enterprise AI Deployment & Capstone Project, spanning 25 hours. You build an end-to-end enterprise AI deployment — RAG pipeline plus AI agent, cloud deployment with CI/CD, and evaluation/guardrails — then present and defend it.

Classes are conducted live — 100+ sessions in total. Recordings are supplied for revision purposes so you can review any session or make up for a class you could not attend.

The program fee is ₹19,500 plus 18% GST, making the total ₹23,010. The same fee applies in every mode and location — live online from Durgapur or classroom at Delhi/Noida — with nothing charged beyond this.

Yes. EMI starts at ₹3,835 per month for 6 months, dividing the ₹23,010 total into instalments. *EMI amount, tenure and eligibility may vary depending on the applicable payment or financing option.

You may request a demo by completing the enquiry form or contacting our counsellors directly. Whether a demo can be arranged depends on the batch currently running, and the team will let you know the upcoming options.

Dedicated Career & Placement Assistance is part of the program, including resume and LinkedIn help, mock interviews and access to openings through 850+ hiring partners. This support does not guarantee a job.

The program prepares you to apply for entry-level AI Forward Deployed Engineer roles, with indicative pay of ₹6–10 LPA. Securing an offer depends on your project work, interview performance and market conditions, and Uncodemy makes no guarantee of a specific role or salary.

Indicative ranges begin at ₹6–10 LPA for an Entry-Level AI Forward Deployed Engineer and reach ₹20–35 LPA for an AI Solutions Architect. Current India reporting places FDE salaries broadly around ₹9–27 LPA (source: Express Computer). Treat these as market ranges — no specific salary is guaranteed.

You become eligible for Forward Deployed Engineer, AI Solutions Engineer, Applied AI Engineer, AI Integration Engineer, Entry-Level AI Forward Deployed Engineer and, with experience, Senior Forward Deployed Engineer roles. The curriculum is broad enough to follow whichever path aligns best with your projects.

The course is open to software and IT professionals, freshers with programming fundamentals, data and ML engineers, career changers from technical consulting backgrounds, and working professionals wanting hands-on RAG/agents/MCP expertise. A laptop and stable internet are the only practical requirements.

No. Salaries listed are indicative market figures, placement support is not a job guarantee, and review counts come from public platform information that may change over time. All of it is presented for transparency so you can decide with full context.

No. There is no centre in Durgapur — Durgapur learners join this program live online. Uncodemy's classroom centres are in Delhi (Laxmi Nagar/Shakarpur) and Noida (Sector 1), and in-person training is available at either if you can travel.

This course is a broader, client-facing enterprise-deployment program covering discovery, RAG, agents, MCP, evaluation and deployment. LLM Engineering and RAG Engineering are narrower, deeper-technical specialisations focused on their specific domains.