AI Forward Deployed Engineer Training Course in Chennai (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

Adoption of applied AI has accelerated across Chennai's enterprise software, SaaS and financial-services clusters, producing real demand for professionals able to shepherd AI from proof-of-concept through to production inside client organisations. The city's blend of large IT-services houses, SaaS product firms, BFSI technology teams and consulting practices means Forward Deployed Engineer capabilities find work across many industries — not only inside research labs.

Hiring managers and interview panels across Chennai tend to focus on three things. First, whether you can design and defend a RAG or agent system architecture aligned with a business problem. Second, whether you carry hands-on deployment experience — Docker, CI/CD, cloud, monitoring and evaluation. Third, whether you can hold a client-facing conversation: scoping, translating business requirements, presenting solutions. Candidates able to walk an interviewer through a completed enterprise AI deployment — rather than just a model notebook — stand out the fastest.

This program has been purpose-built to develop exactly those three capabilities via live sessions, hands-on projects, cloud deployment work and a capstone that replicates real client engagements.

Because the syllabus 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 one fixed job title. The table below pairs each role with the core tools most relevant to it.

Across a standard 2–5 year horizon, professionals generally progress from entry-level Forward Deployed Engineer or AI Solutions Engineer roles to senior FDE positions and, eventually, AI Solutions Architect roles. That journey involves handling more complex client projects, gaining greater ownership of system architecture and gradually building leadership capabilities. The program's core competencies — RAG, AI agents, MCP, evaluation, deployment and customer discovery — form a strong base for sustained career growth in this domain.

Remuneration for Forward Deployed Engineer and closely related AI delivery roles in Chennai varies with employer, experience and technical scope. Entry-level AI Forward Deployed Engineers typically begin at ₹6–10 LPA, while senior FDEs and AI Solutions Architects can command substantially more. The bands shown below are indicative market ranges Uncodemy applies across its course pages.

Recent Indian salary data indicates Forward Deployed Engineers (FDEs) usually earn between ₹9 LPA and ₹27 LPA, depending on experience, employer and technical expertise (Source: Express Computer).

These figures draw on data from AmbitionBox, Glassdoor, Indeed, Express Computer and Uncodemy's placement records, reviewed in October 2026. Publicly available salary data is cross-checked against internal placement information, with estimates refreshed periodically to reflect general market trends. Actual offers in Chennai may differ based on employer size, industry, interview performance, previous experience and job responsibilities. The numbers should therefore be treated as indicative benchmarks for career planning rather than guaranteed salary outcomes.

Demand for Forward Deployed Engineer skills in Chennai spans multiple industries, and Uncodemy's 850+ hiring partners reach into many of them. The sectors below are where Chennai-based AI deployment capabilities are most frequently applied today:

• SaaS product companies: Chennai's strong SaaS ecosystem — mid-size firms and unicorns alike — building AI features and needing engineers who can deliver them to enterprise clients.
• IT services & GCCs: Large services firms and Global Capability Centres across OMR and Guindy deploying RAG and agent solutions for their global client base.
• Banking & Financial Services: Payment platforms, NBFCs and wealth-tech companies applying AI agents and RAG across customer-facing and internal workflows.
• Automotive-tech: Chennai's "Detroit of India" identity — OEMs and auto-tech firms using AI for connected-vehicle analytics, quality inspection and predictive maintenance.
• Healthcare & Med-tech: Private hospital chains and diagnostic companies integrating AI for document intake, reporting and patient-facing assistants.
• Education & EdTech: Coaching networks, universities and ed-tech platforms building AI assistants and RAG-based knowledge systems.

Since every sector sits on the same foundation — RAG, agents, evaluation, deployment and client-facing delivery — a broad Forward Deployed Engineer program lets you apply skills across industries rather than committing to one early in your career.

Becoming an AI Forward Deployed Engineer is a journey of stacked skills, each building on the previous one. The seven steps below mirror this program's sequence, so you always know what comes next and why it matters.

1. FDE & Enterprise AI Fundamentals: Understand what Forward Deployed Engineers do, how enterprise AI delivery works, and where the role fits.
2. Python, SQL, Git & API Engineering: Build the engineering foundation — Python, SQL, Git workflows and REST API development with FastAPI.
3. LLM Fundamentals & Generative AI: Learn how LLMs work, how to use OpenAI APIs and Hugging Face models, and how to design generative AI applications.
4. Prompt & Context Engineering: Master prompt design, structured output, function calling and context handling for reliable outputs.
5. RAG & Enterprise Knowledge Systems: Build 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 with Docker and CI/CD, add evaluation and observability, and complete the enterprise AI capstone.

Timeline: [CONFIRM — duration pending, see flag above before writing a specific month count here].

When evaluating an AI course, weigh the factors that shape your learning experience and career outcomes — course duration, total training hours, live instruction, tools covered, hands-on project work and career assistance. The table below summarises these aspects of the program, making it easier to benchmark against other available courses.

Compared with Uncodemy's LLM Engineering and RAG Engineering courses, the AI Forward Deployed Engineer program delivers broader training in enterprise AI implementation — covering customer discovery, RAG, AI agents, MCP, evaluation, deployment and consulting. While the LLM Engineering and RAG Engineering courses go deeper into their specific technical areas, this program targets people pursuing client-facing enterprise AI delivery careers. Learners already strong in LLMs or RAG and seeking deeper specialisation may prefer one of the focused courses. The capstone project and client-scenario interview preparation add practical value that distinguishes this program from more specialised alternatives.

Because Uncodemy does not operate a physical classroom in Chennai, candidates from the city attend this AI Forward Deployed Engineer program entirely live online. That does not make it a watered-down version of the course. You join the same batches, learn from the same trainers and follow the identical 13-module curriculum as learners in Uncodemy's classroom programs. Sessions run in real time, so you can ask questions, share your screen for code review and discuss RAG or agent architecture with the trainer while class is in progress.

Learning online also removes the daily commute across the city — a meaningful advantage for working professionals. You can pick a weekday or weekend batch around your job and revisit session recordings for revision.

If you would prefer an in-person classroom and are able to travel, in-person training remains available at Uncodemy's Delhi and Noida centres. The fee and curriculum stay identical in both modes.

The program runs approximately 5-6 months and delivers 177 learning hours across 13 modules, backed by more than 100 live sessions designed for structured, hands-on learning.

Prior coding experience is not required. That said, basic computer familiarity and a readiness to pick up technical concepts will help you get started smoothly.

The syllabus spans more than 20 tools and technologies, including 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. The program features a 15-hour module on RAG and Enterprise Knowledge Systems, followed by a 15-hour module on AI Agents, Tool Calling and MCP. Both modules include hands-on learning and project work.

The capstone sits within Module 13, Enterprise AI Deployment & Capstone Project, running for 25 hours. You build and present an end-to-end enterprise AI solution featuring a RAG pipeline, an AI agent, cloud deployment with CI/CD, and evaluation and guardrails.

The course runs through more than 100 live sessions. Session recordings are provided as well, so you can revisit topics later or catch up if you miss a class.

The program fee is ₹19,500 with 18% GST added, making the total ₹23,010. The same fee applies whether you join live online from Chennai or opt for classroom training in Delhi or Noida, with no additional location-based charge stated.

Yes. One indicative EMI option works out to ₹3,835 monthly across 6 months, totalling ₹23,010. The final instalment amount, tenure and eligibility may vary depending on the payment or financing option available to you.

You can request a demo through the course enquiry form or by contacting the counselling team. Demo availability depends on the current batch schedule, and the team can share the available options with you.

Yes. The program offers career and placement assistance, including resume and LinkedIn guidance, mock interviews, and access to openings through a network of 850+ hiring partners. This support can help with your job search, but employment is not guaranteed.

The program is built to prepare you for entry-level AI Forward Deployed Engineer opportunities, with an indicative salary range of ₹6–10 LPA. Actual outcomes depend on your skills, projects, interview performance and market conditions; no specific role or salary is guaranteed.

Indicative salary ranges shown for this career path start at ₹6–10 LPA for entry-level FDE roles and may reach ₹20–35 LPA for AI Solutions Architect positions. Express Computer has reported FDE salaries in India broadly around ₹9–27 LPA. These are market estimates, not guaranteed offers.

Based on your skills and project experience, you could target roles like Forward Deployed Engineer, AI Solutions Engineer, Applied AI Engineer, AI Integration Engineer or Entry-Level Forward Deployed Engineer. Senior FDE positions generally require relevant professional experience, and the best-fit path will depend on your strengths and portfolio.

The program suits software and IT professionals, freshers with programming fundamentals, data or machine learning engineers, and people shifting from technical consulting into AI delivery. You will need a laptop and a reliable internet connection to participate in the learning activities.

No. Salary figures are indicative estimates, placement assistance does not guarantee employment, and review counts rest on public platform information that can change over time. These details are shared as guidance to help you assess the program.

No. The program is delivered live online for learners in Chennai. The classroom locations listed for in-person training are in Delhi (Laxmi Nagar/Shakarpur) and Noida (Sector 1), so attending in person would require travelling to one of those centres.

This program adopts a broader, client-facing approach to enterprise AI delivery, covering customer discovery, RAG, agents, MCP, evaluation and deployment. LLM Engineering and RAG Engineering go deeper into their specific technical areas, making them alternatives for learners who want to specialise in one domain.