AI Forward Deployed Engineer Training Course in Gurugram (Live Online) by Uncodemy

• Grasping where the Forward Deployed Engineer fits within enterprise AI delivery
• Drawing distinctions between FDE duties and those of AI/ML engineering and solutions architecture
• The enterprise AI journey from first discovery to production launch
• Familiar enterprise AI scenarios and where they usually break down
• Client engagement frameworks and how delivery is structured
• A walkthrough of today's AI stack: LLMs, RAG, agents and MCP

• Python basics shaped specifically for AI engineering work
• Handling libraries and isolated virtual environments
• Writing SQL to query and join enterprise datasets
• Git-based version control and team collaboration practices
• Designing REST APIs and managing requests and responses
• Getting started with FastAPI and constructing endpoints
• Authentication methods, error handling and API best practices

• How large language models operate at a conceptual level
• Understanding tokens, embeddings and context windows
• Leveraging OpenAI APIs and the Hugging Face model catalogue
• Common design patterns behind generative AI applications
• Weighing model options for enterprise deployments
• Balancing cost, latency and output quality

• Principles for crafting prompts that deliver dependable results
• System prompts, few-shot examples and role-based prompting
• Handling context in long-running and multi-turn conversations
• Structured outputs and function calling through prompts
• Assessing and refining prompts iteratively
• Prompt injection and associated security threats

• Common enterprise data channels: databases, documents and APIs
• Constructing ingestion and preprocessing pipelines
• Parsing documents, splitting content and attaching metadata
• Generating embeddings as part of the pipeline
• Maintaining data freshness via incremental updates
• Applying governance and access control across AI pipelines

• Defining RAG and explaining its value for enterprises
• Operating vector databases alongside PostgreSQL/pgvector
• Contrasting dense, sparse and hybrid retrieval methods
• Reranking results and calculating relevance scores
• Assembling a complete end-to-end RAG pipeline
• Measuring RAG quality using RAGAS
• Diagnosing and correcting typical RAG failure modes

• Agent architectures and the reasoning loop
• How tool calling works and how to define function schemas
• Orchestrating agents using LangChain and LangGraph
• Building knowledge agents with LlamaIndex
• Fundamentals of the Model Context Protocol (MCP)
• Designing multi-step, multi-tool agent workflows
• Guardrails and how to handle agent failures

• Creating AI-powered applications using FastAPI
• Connecting LLMs, RAG and agents into application logic
• Backend services and asynchronous task processing
• UI integration approaches for AI applications
• Managing state, sessions and conversation history
• Rolling AI application services into production

• Understanding how AI evaluation differs from traditional software testing
• Measuring LLM output quality using RAGAS and custom metrics
• Tracing and observability through LangSmith and Langfuse
• Watching over AI systems running in production
• Spotting drift, regressions and runtime failures
• Setting up continuous evaluation and feedback loops

• Threat modelling for enterprise AI systems
• Recognising prompt injection and data exfiltration risks
• Implementing output moderation and safety filters
• Handling data privacy and PII inside AI pipelines
• Enforcing access control and tenant isolation
• Compliance factors in enterprise AI deployments

• Packaging AI services into containers with Docker
• AWS and Azure foundations for AI deployment
• Setting up CI/CD pipelines for AI applications
• Managing secrets and environment configuration
• Scaling services and tuning performance
• Planning rollbacks and verifying production readiness

• Conducting client discovery and scoping meetings
• Converting business problems into AI solution designs
• Architecting RAG and agent-based systems
• Estimating effort, cost and risk
• Presenting AI solutions to enterprise stakeholders
• Consulting communication and documentation skills

• A full end-to-end enterprise AI deployment project
• Blending a RAG pipeline and AI agent in a client-style scenario
• Deploying to the cloud with CI/CD
• Applying evaluation, observability and guardrails to the capstone
• Presenting and defending the capstone
• Producing portfolio-ready deliverables

Gurugram'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 Gurugram 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 Gurugram 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 Gurugram 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 Gurugram stretches across several industries, and Uncodemy's 850+ hiring partners reach into many of them. The sectors below are where Gurugram-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.

Since Uncodemy runs no classroom in Gurugram, learners from the city attend this AI Forward Deployed Engineer program entirely live online. This does not turn it into a watered-down course. You join the same batches, study with the same trainers and follow the same 13-module curriculum as learners in Uncodemy's classroom programs. Sessions take place in real time, so you can pose questions, share your screen for code review and discuss RAG or agent architecture with the trainer while class is still running.

Studying online also saves you the daily commute across the city, which matters greatly to working professionals. You can pick a weekday or weekend batch around your job and revise using session recordings.

If you would rather attend a physical classroom and can travel, in-person training remains available at Uncodemy's Delhi and Noida centres. Fee and curriculum stay identical in both modes.

The course delivers 177 verified learning hours and over 100 live sessions spread across 13 modules.

No prior coding background is mandatory, as the curriculum starts with foundational programming in Module 2.

Learners get practical exposure to 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.

Certainly. Module 6 is devoted to RAG & Enterprise Knowledge Systems (15 hours) and Module 7 concentrates on AI Agents, Tool Calling & MCP (15 hours). Both modules involve hands-on project work.

Module 13 is the Enterprise AI Deployment & Capstone Project, a 25-hour module in which you build a full enterprise AI deployment — a RAG pipeline plus an AI agent, cloud deployment through CI/CD, and evaluation/guardrails — and then present and defend it.

Every class is delivered live, amounting to 100+ sessions overall. Recordings are provided for revision, letting you revisit any session or cover a class you missed.

The course fee stands at ₹19,500 plus 18% GST, bringing the total to ₹23,010. This same fee applies across every mode and location — live online from Gurugram or classroom at Delhi/Noida — with no additional charges.

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

You can ask for a demo by filling in the enquiry form or reaching out to our counsellors. Demo availability depends on the batch currently running, and the team will advise you on the options coming up.

Dedicated Career & Placement Assistance forms part of the program, covering resume and LinkedIn support, mock interviews and access to openings via 850+ hiring partners. This support does not guarantee a job.

The course equips you to pursue entry-level AI Forward Deployed Engineer positions, with indicative pay around ₹6–10 LPA. Landing an offer hinges on your project work, interview performance and market conditions, and Uncodemy makes no guarantee of a particular role or salary.

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

You qualify for roles including 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 let you pursue whichever path best matches your projects.

The course welcomes software and IT professionals, freshers holding programming basics, data and ML engineers, career changers from technical consulting backgrounds, and working professionals seeking hands-on RAG/agents/MCP expertise. A laptop and dependable internet connection are the only practical requirements.

No. Salaries shown are indicative market figures, placement support is not a job guarantee, and review counts come from public platform information that may change over time. Everything is provided for transparency so you can decide with full context.

No, there is no centre in Gurugram — Gurugram learners attend this program live online. Uncodemy's classroom centres sit in Delhi (Laxmi Nagar/Shakarpur) and Noida (Sector 1), and in-person training is offered at either if you can travel.

This course is the 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.