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

• The Forward Deployed Engineer's role 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

Latur's enterprise and industrial landscape — anchored by the MIDC area and the growing business corridor — has begun to embrace applied AI beyond initial pilot programmes. Manufacturing units, agri-businesses, healthcare providers and educational institutions are increasingly looking at RAG and agent-based systems to improve operations. This has generated consistent demand for engineers capable of taking a loosely defined business problem, selecting an appropriate architecture, and delivering a working AI system within a client's environment. Research-focused roles exist but represent a much smaller segment of the local market.

What interview panels in the region actually assess comes down to three areas. Can you design a RAG or agent architecture and justify the trade-offs for a specific business case? Do you have genuine deployment experience — Docker, CI/CD, cloud, evaluation dashboards, observability? And can you hold your own in a client conversation, scoping requirements and explaining solutions to non-technical stakeholders? A candidate who can narrate an end-to-end deployment, with decisions and obstacles along the way, beats one who only has notebooks to show.

Latur also connects to a wider market — many learners here pursue remote or hybrid roles with employers in Pune, Mumbai, Hyderabad or abroad. In that competition, a portfolio of shipped AI work counts for far more than a stack of certificates. The programme is designed around that reality: live sessions, enterprise projects, cloud deployment practice and a capstone built to resemble a genuine client engagement.

The syllabus touches every layer an FDE needs — 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. Because it is that broad, graduates are not locked into one title. They can apply across several client-facing AI delivery roles, each mapped below to the tools that matter most.

Looking two to five years ahead, the usual trajectory runs from entry-level Forward Deployed Engineer or AI Solutions Engineer into senior FDE work, and then toward AI Solutions Architect — bigger client engagements, more architecture ownership, and eventually team leadership. Everything built in this programme, from RAG and agents to MCP, evaluation, deployment and customer discovery, feeds directly into that progression.

What a Forward Deployed Engineer or a closely related AI delivery professional earns in Latur depends heavily on the employer, the years behind you and the technical scope of the role. Entry-level AI Forward Deployed Engineers generally land in the ₹6–10 LPA band, while senior FDEs and AI Solutions Architects can climb well above that. The ranges below are the indicative market ranges Uncodemy applies 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. The numbers are produced by comparing publicly reported salary data with internal placement records, and they get revisited from time to time to keep them roughly in step with the market. In Latur, the actual offer will hinge on the employer's size and sector, how you perform in interviews, your earlier experience and the exact duties of the role — so read the table as a planning aid, not a promise.

Latur's demand for Forward Deployed Engineer skills is spread over multiple industries rather than concentrated in one, and Uncodemy's 850+ hiring partners extend into several of them. These are the sectors where AI deployment capability gets applied most in the region right now:

• Enterprise software & SaaS: Product companies adding AI features and needing engineers who can deliver them to enterprise clients.
• IT services: Services firms deploying RAG and agent solutions for clients across India and overseas.
• BFSI & financial technology: Banks, insurers and lending platforms applying AI agents and RAG across customer service and internal operations.
• Analytics, research & KPO: Latur's emerging analytics and knowledge-services firms building AI-assisted delivery pipelines.
• Consulting & system integrators: Advisory and SI firms delivering enterprise AI programmes for large clients.

Whichever sector you land in, the underlying toolkit is identical — RAG, agents, evaluation, deployment and client-facing delivery. That is why a broad Forward Deployed Engineer programme keeps your options open instead of committing you to one industry too early.

There is a natural order to becoming an AI Forward Deployed Engineer — each capability rests on the one before it. The seven steps here mirror the sequence of the programme, so you always know what you are doing and why it comes at that point.

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].

Choosing an AI course usually comes down to a handful of practical factors: how long it runs, how many learning hours it actually delivers, whether teaching is live, how many tools you touch, how deep the projects go, and what career support is attached. The table sets out where this programme stands on each of those.

Put this programme next to Uncodemy's LLM Engineering or RAG Engineering courses and the distinction becomes clear. This AI Forward Deployed Engineer course is the wide, client-facing enterprise-deployment path: discovery, RAG, agents, MCP, evaluation, deployment and consulting all in one. LLM Engineering and RAG Engineering go narrower and deeper into their specific technical territory. Choose this one if a client-facing enterprise AI delivery career is the goal. Choose a narrower specialisation if you already have solid LLM or RAG depth and want to push further in a single area. The capstone and the client-scenario interview preparation are two more things that set this programme apart from narrower alternatives.

There is no Uncodemy classroom in Latur, so everybody from the city takes this AI Forward Deployed Engineer course live online. That is not a watered-down version of the classroom experience. You sit in the same batches, learn from the same trainers and work through the same 13-module curriculum as learners attending in person. Because sessions happen in real time, you can raise questions as they arise, share your screen for a code review, and debate RAG or agent design decisions with the trainer mid-class.

There is a practical upside too. Nobody has to fight Latur traffic twice a day to attend — a weekday or weekend batch can be arranged around your job, and session recordings let you revisit anything you missed.

If you would rather learn in a physical classroom and can 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 programme duration is [CONFIRM — pending final confirmation]. Within the final schedule, participants complete 177 verified learning hours and over 100 live sessions spread across 13 modules, delivered live online for learners from Latur.

Extensive coding background is not mandatory. A basic understanding of programming or Python can ease the early weeks, yet the course itself introduces Python, SQL, Git and API engineering from Module 2. [CONFIRM exact prior-knowledge requirement]. A laptop with a stable internet connection is required.

You will gain hands-on experience with 20+ tools such as 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) and Module 7 focuses on AI Agents, Tool Calling & MCP (15 hours). Both modules are practical and project-driven, enabling you to build working pipelines and agents rather than studying theory alone.

The capstone is Module 13 — Enterprise AI Deployment & Capstone Project — lasting 25 hours. You create a complete enterprise AI solution that pairs a RAG pipeline with an AI agent, deploy it on the cloud using CI/CD, incorporate evaluation and guardrails, and then present and defend the work.

All sessions are conducted live, with more than 100 classes in total. Recordings of every session are shared afterwards so you can revise any topic or catch up on a class you could not attend.

The programme fee stands at ₹19,500 plus 18% GST, amounting to ₹23,010 overall. The same fee applies whether you join live online from Latur or opt for classroom training at the Delhi or Noida centre; no extra charges are added.

Yes. EMI plans start from ₹3,835 per month for six months, dividing the ₹23,010 total into instalments. *The precise EMI amount, tenure and eligibility may differ according to the payment or financing option selected.

You may request a demo by submitting the enquiry form or contacting a counsellor directly. Whether a demo can be arranged depends on the batch currently in progress, and the team will inform you of the nearest available options.

Dedicated Career & Placement Assistance forms part of the programme. It includes resume and LinkedIn profiling, mock interviews, and access to openings through 850+ hiring partners. This support does not guarantee employment.

The curriculum prepares you to apply for entry-level AI Forward Deployed Engineer roles, with indicative salaries in the ₹6–10 LPA range. Securing an offer depends on the strength of your projects, interview performance and prevailing market conditions; Uncodemy does not promise any particular role or package.

Typical packages start at ₹6–10 LPA for an Entry-Level AI Forward Deployed Engineer and can reach ₹20–35 LPA for an AI Solutions Architect. Industry data for India currently places FDE salaries broadly between ₹9–27 LPA (source: Express Computer). These figures are market estimates only and are not guaranteed.

After finishing the programme you can pursue roles such as Forward Deployed Engineer, AI Solutions Engineer, Applied AI Engineer, AI Integration Engineer and Entry-Level AI Forward Deployed Engineer. With additional experience you may also progress toward Senior Forward Deployed Engineer positions. The path you follow will depend on the projects you complete.

The course is open to software and IT professionals, freshers who already possess programming fundamentals, data and ML engineers, technical consultants looking to change careers, and working professionals who want practical exposure to RAG, agents and MCP. The only practical requirements are a laptop and a reliable internet connection.

No. The salary ranges listed are indicative market figures, placement assistance does not amount to a job guarantee, and review counts are drawn from public platforms that may change over time. All information is provided for transparency so you can evaluate the programme with complete context.

Uncodemy does not maintain a classroom centre in Latur; learners from the city join the programme live online. Physical centres are located in Delhi (Laxmi Nagar/Shakarpur) and Noida (Sector 1), and classroom training is available at either location if you are able to travel.

This course is a broader, client-facing enterprise-deployment programme that covers discovery, RAG, agents, MCP, evaluation and full deployment. LLM Engineering and RAG Engineering, by contrast, are narrower and deeper technical specialisations focused on their respective domains.