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

• Pinpointing where the Forward Deployed Engineer sits inside enterprise AI delivery
• Distinguishing FDE responsibilities from those of AI/ML engineering and solutions architecture
• The enterprise AI path from initial discovery to production go-live
• Everyday enterprise AI scenarios and the places they usually stall
• Client engagement frameworks and how delivery gets structured
• A walkthrough of today's AI stack: LLMs, RAG, agents and MCP

• Python fundamentals shaped specifically for AI engineering work
• Handling libraries and separate virtual environments
• Writing SQL queries and joins against enterprise datasets
• Git-based version control plus team collaboration workflows
• Designing REST APIs and handling request/response cycles
• Getting started with FastAPI and endpoint creation
• Authentication, error handling and API conventions

• How large language models operate at a conceptual level
• Understanding tokens, embeddings and context windows
• Working with OpenAI APIs and the Hugging Face model ecosystem
• Common design patterns for generative AI applications
• Choosing the right model for enterprise use cases
• Weighing cost, latency and output quality

• Core principles for writing prompts that deliver dependable outputs
• System prompts, few-shot examples and role-based prompting
• Handling context across long and multi-turn interactions
• Structured output plus function calling through prompts
• Assessing and refining prompts iteratively
• Prompt injection and related security hazards

• Typical enterprise data sources: databases, documents and APIs
• Building ingestion and preprocessing pipelines
• Parsing documents, chunking content and attaching metadata
• Generating embeddings as part of the pipeline
• Keeping data fresh through incremental updates
• Applying governance and access control across AI pipelines

• Understanding RAG and its business value for enterprises
• Working with vector databases and PostgreSQL/pgvector
• Dense, sparse and hybrid retrieval approaches explained
• Reranking results and scoring relevance
• Assembling a complete end-to-end RAG pipeline
• Measuring RAG quality using RAGAS
• Diagnosing and fixing common RAG failure modes

• Agent architectures and the reasoning loop explained
• Tool calling and defining function schemas
• Orchestrating agents with LangChain and LangGraph
• Building knowledge agents with LlamaIndex
• Fundamentals of the Model Context Protocol (MCP)
• Designing multi-step, multi-tool agent workflows
• Adding guardrails and handling agent failures

• Creating AI-enabled applications with FastAPI
• Wiring LLMs, RAG and agents into application logic
• Building backend services with asynchronous processing
• UI integration patterns for AI-powered apps
• Handling state, sessions and conversation history
• Taking AI app services live in production

• How AI evaluation differs from conventional software testing
• Metrics for judging LLM output quality (RAGAS and custom metrics)
• Tracing and observability with LangSmith and Langfuse
• Keeping watch over AI systems in production
• Detecting drift, regressions and runtime failures
• Building continuous evaluation and feedback loops

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

• Packing 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 ensuring production readiness

• Conducting client discovery and scoping sessions
• Turning 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 complete end-to-end enterprise AI deployment project
• Combining 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 your capstone
• Producing portfolio-ready deliverables

Dehradun's IT services, education technology and government digitisation clusters have sped up their adoption of applied AI, generating real demand for engineers who can carry AI from proof-of-concept to production inside client organisations. The city's blend of IT-services firms, education technology ventures, healthcare digitisation efforts and consulting practices means Forward Deployed Engineer skills are being applied across sectors — not just in research labs.

Interview panels in Dehradun 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 Dehradun 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 Dehradun 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 Dehradun stretches across several industries, and Uncodemy's 850+ hiring partners reach into many of them. The sectors below are where Dehradun-based AI deployment skills are most commonly applied today:

• IT services & consulting: Services firms and consultancies deploying RAG and agent solutions for national and global clients.
• Education technology: EdTech ventures building AI-powered learning platforms, tutoring assistants and assessment tools requiring reliable deployment.
• Healthcare technology: Digital health providers applying RAG and AI agents in patient support, diagnostics assistance and compliance workflows.
• Government & public sector technology: E-governance initiatives requiring AI-enabled citizen services and secure deployment.
• Tourism & hospitality technology: Travel platforms and hotel chains using AI agents, chatbots and recommendation engines for visitor engagement.

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 Dehradun, learners from the city attend this AI Forward Deployed Engineer program entirely live online. That doesn't make it a diminished version of the 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 more than 100 live sessions distributed across 13 modules.

No previous coding background is necessary, since foundational programming is introduced from Module 2.

Learners gain practical exposure to over 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. Module 6 covers RAG & Enterprise Knowledge Systems (15 hours) while Module 7 is devoted to AI Agents, Tool Calling & MCP (15 hours). Both 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 — RAG pipeline plus AI agent, cloud deployment with CI/CD, and evaluation/guardrails — and then present and defend it.

Classes are delivered live, adding up to 100+ sessions. Recordings are supplied for revision, so you can go back to any session or make up a class you missed.

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

Yes. EMI starts at ₹3,835 per month for 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 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, covering 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 Dehradun — Dehradun 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 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.