AI Forward Deployed Engineer Training Course in dhanbad (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

Situated in the core of the dhanbad–Bhubaneswar twin-city region, dhanbad has witnessed IT services, public-sector digital initiatives and a growing startup ecosystem shift applied AI from trial projects into production delivery. Employers throughout this corridor now seek engineers capable of taking a business problem, designing a RAG or agent-based solution, and shipping it within an actual client environment — rather than merely training a model in a notebook.

Interview panels in this region typically assess three things. First, whether you can design and defend a RAG or agent architecture against a specific business requirement. Second, whether you possess genuine deployment experience — Docker, CI/CD, cloud, evaluation and observability. Third, whether you can hold a client-facing conversation: scoping the problem, translating requirements and presenting a solution to non-technical stakeholders. Candidates who can walk an interviewer through a completed enterprise AI deployment stand out fastest.

Since dhanbad's own market is smaller than a metro, many learners here target roles across the twin-city corridor as well as remote or hybrid positions with companies elsewhere in India. That makes a portfolio of real deployments — not just certificates — the strongest differentiator. This programme is built around exactly that: live sessions, hands-on enterprise projects, cloud deployment work and a capstone that mirrors a real client engagement.

Because the programme spans 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 fixed title. The table below maps each role to the core tools most relevant to it.

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

Compensation for Forward Deployed Engineer and adjacent AI delivery roles in dhanbad depends on the employer, experience level and technical scope. Entry-level AI Forward Deployed Engineers typically begin at ₹6–10 LPA, while senior FDEs and AI Solutions Architects can earn substantially more. The ranges below represent the indicative market ranges Uncodemy applies 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 assembled by setting publicly reported salary data against internal placement information, and they are reviewed periodically so they stay broadly aligned with the market. Actual offers in dhanbad 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 in and around dhanbad is spread across several industries, and Uncodemy's 850+ hiring partners extend into many of them. The sectors listed below are where AI deployment skills find the most common application in this region today:

• Enterprise software & SaaS: Product companies adding AI features and needing engineers who can deliver them to enterprise clients.
• IT services: Services firms deploying RAG and agent solutions for clients across India and overseas.
• Fintech & banking technology: Lending, payments and financial-services teams applying AI agents and RAG in customer-facing and internal workflows.
• Public-sector digital programmes: Government and e-governance initiatives that need AI systems deployed with evaluation, guardrails and data privacy.
• Consulting & system integrators: Advisory and SI firms delivering enterprise AI programmes for large clients.

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

Becoming an AI Forward Deployed Engineer involves a progression of skills, each building on the previous one. The seven steps outlined below mirror the order of this programme, 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].

Before selecting an AI course, weigh the factors that influence your outcome: duration, total learning hours, live teaching, tool coverage, project depth and career support. The table presents these factors for this programme so you can compare them against any alternative.

Compared with Uncodemy's LLM Engineering or RAG Engineering courses, this AI Forward Deployed Engineer programme is the broader, client-facing enterprise-deployment option — encompassing discovery, RAG, agents, MCP, evaluation, deployment and consulting. LLM Engineering and RAG Engineering are narrower, deeper-technical specialisations focused on their respective domains. If your goal is a client-facing enterprise AI delivery career, this programme is the better fit. If you already have strong LLM or RAG depth and want to go further in one specific area, a narrower specialisation may be more appropriate. The capstone project and client-scenario interview preparation further separate this programme from narrower alternatives.

Since Uncodemy does not run a classroom in dhanbad, learners from the city attend this AI Forward Deployed Engineer programme completely live online. That does not mean it is a lighter version of the course. You join the same batches, learn from the same trainers and follow the same 13-module curriculum as learners in Uncodemy's classroom programmes. 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 underway.

Studying online also removes the daily commute, which matters a great deal to working professionals. You can choose a weekday or weekend batch around your job and revise with session recordings.

If you would prefer a physical classroom and are able to travel, in-person training is still available at Uncodemy's Delhi and Noida centres. The fee and curriculum are the same in both modes.

The programme duration is [CONFIRM — duration pending final confirmation]. Within this schedule, learners complete 177 verified learning hours and more than 100 live sessions across 13 modules, delivered online in real time for dhanbad learners.

Some familiarity with programming or Python is beneficial, but extensive prior coding experience is not required. Python, SQL, Git and API engineering are introduced starting from Module 2, so the course builds the foundation early on. [CONFIRM exact prior-knowledge requirement]. A laptop with a stable internet connection is necessary.

You will gain hands-on experience with more than 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.

Absolutely. Module 6 addresses RAG & Enterprise Knowledge Systems (15 hours) and Module 7 addresses AI Agents, Tool Calling & MCP (15 hours). Both are practical modules that include project work.

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

Sessions are conducted live — over 100 in total. Recordings are provided for revision so you can review any session or catch up on a class you were unable to attend.

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

Yes. EMI begins 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 can request a demo by filling out the enquiry form or getting in touch with our counsellors directly. Whether a demo can be arranged depends on the batch currently running, and the team will inform you of the upcoming options.

Dedicated Career & Placement Assistance is included in the programme, covering resume and LinkedIn guidance, mock interviews and access to openings through 850+ hiring partners. This support does not guarantee a job.

The programme prepares you to apply for entry-level AI Forward Deployed Engineer positions, 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 start at ₹6–10 LPA for an Entry-Level AI Forward Deployed Engineer and go up to ₹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.

After completion, you become eligible for roles such as 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. The curriculum is broad enough to pursue whichever path aligns best with your projects.

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

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

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