AI Forward Deployed Engineer Training Course in navi-mumbai (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

navi-mumbai's enterprise software, IT services and fintech clusters have accelerated their adoption of applied AI, creating real demand for engineers who can take AI from proof-of-concept to production inside client organisations. The city's mix of large IT-services firms, product companies, BFSI technology teams and consulting practices means Forward Deployed Engineer skills are being applied across sectors — not just in research labs.

Interview panels in navi-mumbai typically weigh three things. First, whether you can design and defend a RAG or agent system architecture that fits a business problem. Second, whether you have real deployment experience — Docker, CI/CD, cloud, monitoring, evaluation. Third, whether you can hold a client-facing conversation: scoping, translating business requirements, presenting solutions. Candidates who can walk an interviewer through a completed enterprise AI deployment, not just a model notebook, differentiate themselves fastest.

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

Because 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 aim at several client-facing AI delivery roles instead of a single title. The table below maps each role to the core tools most relevant to it.

Over a 2–5 year progression, most professionals move from entry-level Forward Deployed Engineer or AI Solutions Engineer roles into senior FDE and eventually AI Solutions Architect positions, taking on larger client engagements, deeper architecture responsibility and team leadership. The skills built in this program — 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 navi-mumbai 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 substantially 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 setting publicly reported salary data against internal placement information, and they are reviewed periodically so they stay broadly in line with the market. Actual offers in navi-mumbai 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 navi-mumbai stretches across several industries, and Uncodemy's 850+ hiring partners reach into many of them. The sectors below are where navi-mumbai-based AI deployment skills are most commonly applied today:

• Enterprise software & SaaS: Product companies building AI features and needing engineers who can deliver them to enterprise clients.
• IT services: Large services firms deploying RAG and agent solutions for their global client base.
• Fintech: Payment platforms, 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.

Because every sector depends on the same 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 previous one. The seven steps below follow the order of this program, 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 choosing an AI course, compare the factors that affect 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 compare them with any alternative.

Compared with Uncodemy's LLM Engineering or RAG Engineering courses, this AI Forward Deployed Engineer program is the broader, client-facing enterprise-deployment program — spanning 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 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 program from narrower alternatives.

Uncodemy does not run a classroom in navi-mumbai, so learners from the city join this AI Forward Deployed Engineer program fully live online. That does not make it a reduced version of the course. You attend the same batches, learn from the same trainers and follow the same 13-module curriculum as learners in Uncodemy's classroom programs. Sessions happen 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.

Learning online also removes the daily commute across the city, which counts for a lot with working professionals. You can select 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 course covers 177 verified learning hours and 100+ live sessions distributed across 13 modules.

No prior coding background is required.

You'll gain hands-on exposure to over 20 tools, among them 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 concentrates on RAG & Enterprise Knowledge Systems (15 hours), while Module 7 takes up AI Agents, Tool Calling & MCP (15 hours). Both come with significant hands-on project work.

The capstone sits in Module 13, Enterprise AI Deployment & Capstone Project, spanning 25 hours. You will assemble a complete enterprise AI deployment — a RAG pipeline joined with an AI agent, cloud deployment via CI/CD, plus evaluation and guardrails — before presenting and defending it.

Every session runs live — more than 100 in total. Recordings get supplied for revision, so you can revisit any class or catch up on one you missed.

Tuition is ₹19,500 with 18% GST added, bringing the total to ₹23,010. That identical fee applies across every mode and location — live online from navi-mumbai or classroom sessions at Delhi/Noida — with nothing extra charged.

Yes. EMI starts at ₹3,835 per month across 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 completing the enquiry form or reaching out to our counsellors. Whether a demo is possible hinges on the batch currently running, and the team will share what upcoming options exist.

Yes, dedicated Career & Placement Assistance comes included, spanning resume and LinkedIn guidance, mock interviews and access to openings through 850+ hiring partners. This support does not guarantee a job.

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

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

You'll qualify 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 follow whichever path aligns best with your projects.

The course accepts 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. The salary figures shown are indicative market estimates, placement support is not a job guarantee, and review counts stem from public platform data that can shift over time. All of it appears for transparency so you can decide with full context.

No. There is no centre in navi-mumbai — navi-mumbai learners attend this program 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 location if you can travel.

This program adopts a broader, client-facing stance on enterprise deployment, covering discovery, RAG, agents, MCP, evaluation and deployment. In contrast, LLM Engineering and RAG Engineering are tighter, deeper-technical specialisations centred on their own particular domains.