AI Forward Deployed Engineer Training Course in Chhatrapati Sambhajinagar (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

Chhatrapati Sambhajinagar (formerly Aurangabad) is one of Maharashtra's most significant industrial centres — best known for its automotive and auto-component manufacturing belt across Waluj MIDC and Shendra, alongside a growing pharma and agri-processing footprint. Its dedicated IT services base is still small, so remote and pan-India roles matter most for local engineers — and FDE work is naturally remote-friendly since it is delivered with and for enterprise clients wherever they are based. Local manufacturers, auto-ancillary units and pharma companies are also beginning to apply AI to operations, quality, procurement and support workflows, opening up a small but growing set of on-the-ground opportunities alongside the larger remote market.

For roles connected to Waluj and Shendra or to remote teams, candidates can prepare to explain a business-focused AI design, deployment decisions involving Docker, cloud and CI/CD, and how they would work with a client. An end-to-end project offers practical evidence beyond a standalone model experiment.

Live instruction, build assignments and the capstone connect solution design, implementation and deployment in a single learning path.

Since 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 target multiple client-facing AI delivery roles rather than one fixed title. The table below pairs each role with the core tools most relevant to it.

A learner might enter through an FDE or AI Solutions Engineer position and later pursue senior delivery or AI Solutions Architect work. Broader customer ownership and greater input into architecture or team direction can come with experience; ongoing development in applied AI remains important.

Salaries for Forward Deployed Engineer and adjacent AI delivery roles vary by employer, experience and technical scope. In smaller IT markets such as Chhatrapati Sambhajinagar, on-site offers may sit toward the lower end of the indicative ranges below, while remote roles with national firms and Pune-based positions typically match metro bands. 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 for Chhatrapati Sambhajinagar-based learners 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 rather than a promise.

Chhatrapati Sambhajinagar's automotive, pharma and manufacturing base offers a local lens for considering applied-AI work, while Pune and remote employers broaden the search. The sectors below illustrate representative places where deployment skills may be used.

• Automotive & auto components: Waluj MIDC and Shendra manufacturing belts applying AI to quality inspection, procurement, maintenance and support workflows.
• Pharma & life sciences: Regional pharma clusters and CROs applying AI to R&D support, regulatory documentation, quality assurance and supply chain functions.
• IT services: Larger services firms (many based in Pune, Mumbai and Bengaluru) deploying RAG and agent solutions for global clients and hiring remotely across India.
• Agri-processing: Marathwada's agro and food-processing clusters applying AI to supply chain, quality and support functions.
• Fintech & BFSI: Payment platforms, lending and wealth-tech companies applying AI agents and RAG in customer-facing and internal workflows.

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 instead of 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 inside an organisation.
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 work with 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, weigh 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 option — 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 Chhatrapati Sambhajinagar, so learners from the city join this AI Forward Deployed Engineer program fully live online. This 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 run 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 eliminates the long commute to Pune or Mumbai for classes, 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 willing 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.

Its 13-module structure provides 177 verified learning hours, supported by more than 100 live instructional sessions.

Beginners are welcome. No previous coding is expected, as the programme introduces foundational skills along the way.

You will work with 20+ tools: 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 covers AI Agents, Tool Calling & MCP (15 hours). Both modules include hands-on project work.

The capstone is Module 13, Enterprise AI Deployment & Capstone Project, spanning 25 hours. You build an end-to-end enterprise AI deployment — RAG pipeline plus AI agent, cloud deployment with CI/CD, and evaluation/guardrails — then present and defend it.

Classes run live — 100+ sessions in total. Recordings are provided for revision so you can review any session or catch up on a class you could not attend.

The program fee is ₹19,500 plus 18% GST, making the total ₹23,010. The same fee applies in every mode and location — live online from Chhatrapati Sambhajinagar 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 may request a demo by filling in 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, including 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). In a smaller market like Chhatrapati Sambhajinagar, on-site offers may sit toward the lower end, while remote roles with national firms and Pune-based positions typically match metro bands. 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. Everything is presented for transparency so you can decide with full context.

No. There is no centre in Chhatrapati Sambhajinagar — learners from the city 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 a 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.