AI Forward Deployed Engineer Training Course in Hubballi-Dharwad (Live Online) by Uncodemy

• Placing the Forward Deployed Engineer within the enterprise AI delivery picture
• Telling apart FDE responsibilities, AI/ML engineering duties and solutions architecture work
• The enterprise AI journey from initial discovery to production go-live
• Standard enterprise AI scenarios and where they normally stumble
• Client engagement frameworks and how delivery structures work
• A tour of the present-day AI stack: LLMs, RAG, agents and MCP

• Python fundamentals organised around AI engineering needs
• Dealing with libraries and separate virtual environments
• Writing SQL queries and joins against enterprise datasets
• Version control with Git and collaborative team workflows
• Designing REST APIs and handling requests/responses
• Beginning with FastAPI and endpoint creation
• Authentication, error handling and API conventions

• How large language models function 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 a model for enterprise use cases
• Weighing cost, latency and output quality

• Core principles for writing prompts that yield dependable outputs
• System prompts, few-shot examples and role-based prompting
• Managing context across long and multi-turn interactions
• Structured output and function calling through prompts
• Measuring and refining prompts through iteration
• 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 with incremental updates
• Applying governance and access control across AI pipelines

• Understanding RAG and its role in 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
• Core concepts 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

Hubballi-Dharwad's engineering cluster, education ecosystem and government digitisation initiatives have accelerated their adoption of applied AI, creating tangible demand for engineers who can carry AI from proof-of-concept to production inside client organisations. The twin city's mix of manufacturing technology teams, 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 Hubballi-Dharwad 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 Hubballi-Dharwad 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 Hubballi-Dharwad 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 Hubballi-Dharwad stretches across several industries, and Uncodemy's 850+ hiring partners reach into many of them. The sectors below are where Hubballi-Dharwad-based AI deployment skills are most commonly applied today:

• Manufacturing & industrial technology: Engineering and process plants adopting AI for predictive maintenance, quality inspection and supply-chain forecasting.
• 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.
• Consulting & system integrators: Advisory and SI firms delivering enterprise AI programmes for regional and national 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.

Because Uncodemy runs no classroom in Hubballi-Dharwad, learners from the twin 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 spans 177 verified learning hours and over 100 live sessions across 13 modules.

No previous coding background is required, since foundational programming begins in Module 2.

You will gain practical exposure to more than 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, absolutely. Module 6 focuses on RAG & Enterprise Knowledge Systems (15 hours) while Module 7 covers AI Agents, Tool Calling & MCP (15 hours). Both involve hands-on project work.

Module 13 is the Enterprise AI Deployment & Capstone Project (25 hours). You construct a full enterprise AI deployment — a RAG pipeline plus AI agent, cloud deployment via CI/CD, and evaluation/guardrails — then present and defend it.

All classes happen live, totalling 100+ sessions. Recordings are made available for revision, so you can revisit any session or cover material from a class you missed.

The fee is ₹19,500 plus 18% GST, taking the total to ₹23,010. This fee remains the same across all modes and locations — live online from Hubballi-Dharwad or classroom at Delhi/Noida — with nothing extra to pay.

Yes. EMI starts at ₹3,835 monthly 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 may request a demo by completing the enquiry form or contacting a counsellor. Whether a demo can be arranged depends on the batch currently running, and the team will advise you on upcoming options.

Dedicated Career & Placement Assistance is included, covering resume and LinkedIn help, mock interviews and access to openings via 850+ hiring partners. This support does not guarantee a job.

The program prepares you to pursue 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 particular 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 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 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 best fits your projects.

The course is open to software and IT professionals, freshers with programming basics, data and ML engineers, career changers from technical consulting backgrounds, and working professionals wanting hands-on RAG/agents/MCP expertise. A laptop and reliable internet are the only practical requirements.

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

No, there is no centre in Hubballi-Dharwad — Hubballi-Dharwad learners join 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 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.