AI Forward Deployed Engineer Training Course in Kota (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

Kota is best known as an education hub, and that reputation is quietly driving its next wave of technical work — coaching institutions, ed-tech platforms, education-content startups and a small but growing software services cluster are all beginning to use applied AI. From adaptive learning tools and RAG-based knowledge assistants to agent-driven student services, the city has real demand for engineers who can move an AI system from a business problem to a live, client-facing deployment rather than just a proof of concept.

Interview panels in Kota and the surrounding Hadoti region tend to test three things. Can you design a RAG or agent architecture and defend the choices against a business need? Do you have genuine deployment mileage — Docker, CI/CD, cloud, evaluation and observability? And can you hold a client-facing conversation, scoping requirements, explaining trade-offs to stakeholders who are not engineers? Candidates who can narrate a completed enterprise AI deployment clearly beat those with only notebooks to show.

Because Kota's own market is narrower than a metro, many learners here pursue remote or hybrid roles with employers in Jaipur, Delhi-NCR, Bengaluru, Pune or overseas. That makes a portfolio of shipped AI work — not a stack of certificates — the strongest differentiator. The programme is designed around that reality: live sessions, enterprise projects, cloud deployment practice and a capstone built to resemble a genuine client engagement.

The syllabus spans every layer an FDE needs — 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. Because the scope is that wide, graduates are not confined to a single job title. They can apply across several client-facing AI delivery roles, each of which is mapped below to the tools that matter most.

Across a two-to-five-year window, the usual trajectory runs from entry-level Forward Deployed Engineer or AI Solutions Engineer to senior FDE, and then towards AI Solutions Architect — larger client engagements, deeper architecture responsibility and eventually team leadership. Everything built in this programme, from RAG and agents to MCP, evaluation, deployment and customer discovery, feeds straight into that progression.

Pay for a Forward Deployed Engineer or a closely related AI delivery role in Kota depends heavily on the employer, the years behind you and the technical scope of the work. Entry-level AI Forward Deployed Engineers generally sit in the ₹6–10 LPA band, while senior FDEs and AI Solutions Architects can earn considerably more. The ranges below are 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. The figures are compiled by comparing publicly reported salary data with internal placement information, and they are revisited periodically to keep them roughly aligned with the market. In Kota, the actual offer will hinge on the employer's size and sector, your interview performance, prior experience and the precise responsibilities of the role — so read the table as a planning guide, not a commitment.

Demand for Forward Deployed Engineer capability around Kota is spread across several industries rather than concentrated in one, and Uncodemy's 850+ hiring partners reach into many of them. The sectors below are where AI deployment skills are most commonly applied in this region today:

• Education technology & coaching tech: Learning platforms, adaptive-testing tools and coaching institutions using AI assistants, RAG-based knowledge systems and automated student-support workflows.
• IT services & small-scale software: Services firms and product startups deploying RAG and agent solutions for clients across India and overseas.
• Enterprise software & SaaS: Product companies adding AI features and needing engineers who can deliver them to enterprise clients.
• Manufacturing & industrial engineering: Kota's stone, engineering and agro-processing units adopting AI in quality inspection, predictive maintenance and supply chain.
• Consulting & system integrators: Advisory and SI firms delivering enterprise AI programmes for large clients.

Whichever sector you land in, the underlying toolkit is the same — RAG, agents, evaluation, deployment and client-facing delivery. That is why a broad Forward Deployed Engineer programme keeps your options open instead of locking you into one industry too early.

Becoming an AI Forward Deployed Engineer is a sequence of skills, each building on the previous one. The seven steps below follow 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].

Choosing an AI course usually comes down to a handful of practical factors: how long it runs, how many learning hours it actually delivers, whether teaching is live, how many tools you touch, how deep the projects go, and what career support is attached. The table sets out where this programme stands on each of those.

Put this programme next to Uncodemy's LLM Engineering or RAG Engineering courses and the distinction becomes clear. This AI Forward Deployed Engineer course is the wide, client-facing enterprise-deployment path: discovery, RAG, agents, MCP, evaluation, deployment and consulting all in one. LLM Engineering and RAG Engineering go narrower and deeper into their specific technical territory. Choose this one if a client-facing enterprise AI delivery career is the goal. Choose a narrower specialisation if you already have solid LLM or RAG depth and want to push further in a single area. The capstone and the client-scenario interview preparation are two more things that set this programme apart from narrower alternatives.

There is no Uncodemy classroom in Kota, so learners from the city attend this AI Forward Deployed Engineer programme entirely live online. That is not a diluted version of the classroom experience. You sit in the same batches, learn from the same trainers and follow the same 13-module curriculum as learners attending in person. Because sessions run in real time, you can raise questions as they come up, share your screen for a code review and debate RAG or agent design decisions with the trainer mid-class.

There is a practical upside too. Nobody has to fight Kota traffic twice a day to attend — a weekday or weekend batch can be arranged around your job, and session recordings let you revisit anything you missed.

If you would rather learn in a physical classroom and can 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 final programme duration is [CONFIRM — pending confirmation]. Whatever the schedule, learners complete 177 verified learning hours and 100+ live sessions spread across 13 modules, all delivered live online for Kota learners.

Strictly, no. Some comfort with programming or Python will make the first weeks smoother, but the course does not assume deep coding experience — Python, SQL, Git and API engineering are introduced in Module 2. [CONFIRM exact prior-knowledge requirement]. You will need a laptop and a stable internet connection.

Learners practise on 20+ tools, 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, extensively. RAG & Enterprise Knowledge Systems is Module 6 (15 hours) and AI Agents, Tool Calling & MCP is Module 7 (15 hours). Both are project-based, so you build working pipelines and agents instead of only reading theory.

Module 13 — the Enterprise AI Deployment & Capstone Project — is a 25-hour build. You assemble an end-to-end deployment where a RAG pipeline works alongside an AI agent, shipped to the cloud through CI/CD with evaluation and guardrails in place, and then defend it in a presentation.

Every session is conducted live, and the programme includes 100+ such sessions. Session recordings are also provided so you can revisit a topic or catch up on a class you missed.

The fee is ₹19,500 plus 18% GST, totalling ₹23,010. That figure holds whether you attend live online from Kota or opt for classroom training at the Delhi or Noida centres, and nothing extra is charged beyond it.

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

You can request a demo through the enquiry form or by speaking with a counsellor. Availability depends on the batch that is currently running, and the team will share the nearest options with you.

The programme includes Dedicated Career & Placement Assistance — resume and LinkedIn guidance, mock interviews and openings shared via 850+ hiring partners. It is support, not a job guarantee.

Yes, the programme equips you to apply for entry-level AI Forward Deployed Engineer positions, with indicative pay of ₹6–10 LPA. Whether you land an offer depends on your projects, interview performance and the market — no specific role or salary is promised.

Indicative ranges run from ₹6–10 LPA at entry level up to ₹20–35 LPA for an AI Solutions Architect. Current India reporting places FDE salaries broadly around ₹9–27 LPA (source: Express Computer). These are market ranges, not guaranteed figures.

You can target Forward Deployed Engineer, AI Solutions Engineer, Applied AI Engineer, AI Integration Engineer and Entry-Level AI Forward Deployed Engineer roles, and with experience move towards Senior Forward Deployed Engineer. Which path you take depends on the projects you build.

Software and IT professionals, freshers with programming fundamentals, data and ML engineers, technical consultants switching careers, and working professionals seeking RAG/agents/MCP expertise are all eligible. Practically speaking, you need a laptop and stable internet.

No. Salary bands are indicative market estimates, placement assistance is support rather than a job guarantee, and review counts come from public platforms and can shift over time. Everything is shown so you can judge the programme with full context.

No, there is no Uncodemy centre in Kota — learners here attend live online. The institute's two physical classrooms are in Delhi (Laxmi Nagar/Shakarpur) and Noida (Sector 1), and you are welcome there if you can travel.

This programme is the broad, client-facing enterprise-deployment course — discovery, RAG, agents, MCP, evaluation and deployment. LLM Engineering and RAG Engineering go narrower and deeper into their specific technical domains.