AI Forward Deployed Engineer Training Course in Ujjain (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

Ujjain has historically been defined by heritage tourism, religious travel and a substantial education ecosystem around Mahakaleshwar and Vikram University, while its technical talent often looks toward Indore, Bhopal or remote-first employers for applied AI careers. As Indore's IT and product-engineering ecosystem expands and nearby manufacturing hubs in Dewas digitise their operations, demand is quietly building for engineers who can move AI from a pilot to a production system inside a client organisation. Ujjain's dense student population, growing edtech interest and proximity to Indore's IT parks make the city a realistic base for candidates pursuing Forward Deployed Engineer careers — locally, in nearby metros, or through remote positions with national employers.

Interview panels generally weigh three things first. One, whether you can design and defend a RAG or agent system architecture that matches a business problem. Two, whether you have real 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.

As the program 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 many client-facing AI delivery roles rather than a single title. The table below maps each role to the core tools most relevant to it.

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

Compensation for Forward Deployed Engineer and adjacent AI delivery roles in Ujjain and the surrounding region differs 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 considerably 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 Ujjain 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 Ujjain 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 Ujjain:

• Enterprise software & SaaS: Product companies building AI features and needing engineers who can deliver them to enterprise clients, often based in Indore.
• IT services: Services firms deploying RAG and agent solutions for their global client base, including teams in Indore and Bhopal.
• Fintech: Payments, lending and wealth-tech companies applying AI agents and RAG in customer-facing and internal workflows.
• Product engineering: Captive centres and GCCs designing AI-enabled products that must run in production at scale.
• Consulting & system integrators: Advisory and SI firms delivering enterprise AI programmes for large clients across sectors.

Because all these sectors rest on a shared foundation — RAG, agents, evaluation, deployment and client-facing delivery — 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 is a sequence of skills, each building on the one before. The seven steps below follow this program's order, so you always know what comes next and why it matters.

1. FDE & Enterprise AI Fundamentals: Learn what Forward Deployed Engineers do, how enterprise AI delivery is structured, and where the role fits.
2. Python, SQL, Git & API Engineering: Build the engineering base — Python, SQL, Git workflows and REST API development with FastAPI.
3. LLM Fundamentals & Generative AI: Understand 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 results.
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: 5-6 months, spanning all seven steps from fundamentals through the capstone.

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

Unlike Uncodemy's LLM Engineering or RAG Engineering courses, this AI Forward Deployed Engineer program is the broader, client-facing enterprise-deployment track — covering 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 program fits better. If you already have strong LLM or RAG depth and want to go further in one specific area, a narrower specialisation may suit you more. The capstone project and client-scenario interview preparation further separate this program from narrower alternatives.

Since Uncodemy has no physical classroom in Ujjain, learners there participate in this AI Forward Deployed Engineer program entirely live online. That does not make it a lighter version. You join the same batches, learn from the same trainers and cover the same 13-module curriculum as students in Uncodemy's classroom programs. Sessions happen in real time, letting you ask questions, share your screen for code review and discuss RAG or agent architecture with the trainer while class is underway.

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

Should you prefer a physical classroom and can travel, in-person training remains available at Uncodemy's Delhi and Noida centres. Fees and curriculum are the same in both modes.

The course duration is five to six months, covering 177 verified learning hours and 100+ live sessions across 13 modules.

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

The course includes 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.

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

The capstone is Module 13, Enterprise AI Deployment & Capstone Project, and lasts 25 hours. You create 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 Ujjain 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 Ujjain — Ujjain 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.