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

• FDE role, responsibilities and delivery lifecycle
• Enterprise AI use cases and business problem framing
• Stakeholder communication and expectation management
• Build vs buy decisions in enterprise AI
• Overview of the enterprise AI stack

• Python for AI engineering
• SQL queries, joins and data retrieval
• Git and GitHub workflows
• REST APIs and FastAPI
• Error handling, testing and project structure

• Transformers and LLM architecture basics
• Tokens, context windows and embeddings
• OpenAI APIs and Hugging Face models
• Generative AI application patterns
• Model selection, cost and latency trade-offs

• Prompt design patterns and structured outputs
• System prompts and role instructions
• Context engineering and context window management
• Few-shot prompting and prompt iteration
• Prompt testing and versioning

• Connecting enterprise data sources
• Data ingestion, cleaning and chunking
• PostgreSQL and pgvector
• Building reliable AI data pipelines
• Data quality and access control basics

• RAG architecture and retrieval strategies
• Embeddings and Vector Databases
• LangChain and LlamaIndex for RAG
• Hybrid search and re-ranking
• Building enterprise knowledge assistants
• Reducing hallucinations with grounded answers

• Agent design and reasoning loops
• Tool and function calling
• LangGraph workflows and state management
• MCP (Model Context Protocol) servers and clients
• Multi-step and multi-agent patterns
• Human-in-the-loop controls

• Building AI back-end services with FastAPI
• Integrating LLM APIs into applications
• Streaming responses and session handling
• Frameworks for orchestration
• Application architecture for AI products

• Evaluation metrics for LLM and RAG systems
• RAGAS-based evaluation
• Tracing and monitoring with LangSmith/Langfuse
• Failure analysis and regression testing
• Reliability, fallbacks and cost monitoring

• Prompt injection and common LLM risks
• Guardrails and output validation
• Data privacy and sensitive-data handling
• Access control and secrets management
• Responsible AI practices in enterprise settings

• Containerising AI services with Docker
• Cloud deployment on AWS/Azure
• CI/CD pipelines for AI applications
• Environment configuration and scaling basics
• Deployment monitoring and rollback

• Customer discovery interviews and requirement capture
• Translating business needs into solution architecture
• Scoping, estimation and risk assessment
• Demos, stakeholder presentations and documentation
• AI consulting practices and client handover

• Capstone problem definition and discovery
• Building the RAG and agent solution
• Evaluation, guardrails and observability setup
• Cloud deployment with CI/CD
• Final client-style presentation and review

AI Forward Deployed Engineer careers hold growing promise for Rajkot learners, since businesses are looking for people who can convert AI ideas into dependable, client-ready systems. These positions are usually organised around projects and are often open to remote work, so learners in and around Rajkot can apply to IT-services firms, enterprise software companies and product-engineering teams across India without relocating. As organisations leave behind small AI pilots and move to production, they need engineers who combine LLM expertise, data-integration experience and clear communication with clients.

Employers often favour evidence over credentials alone: a working RAG system, an agent calling real tools, and a deployment that is evaluated and monitored. Three areas tend to carry the most weight in interviews. The first is RAG and agent system design, including how you pick retrieval strategies and cope with failure. The second is deployment experience with Docker, cloud services and CI/CD. The third is client-facing problem solving, such as making sense of unclear requirements and explaining trade-offs to people outside engineering. This course has been structured around those very expectations.

On completing the program, you can pursue roles that sit where AI engineering meets client delivery. Because the course covers discovery, RAG, agents, MCP, evaluation and deployment, graduates can look at a group of connected job families. The table below pairs each role with the main tools behind it, running from entry-level to senior progression.

In the two-to-five-year range, many engineers evolve from shipping single solutions to leading entire client engagements. They shape architecture decisions, mentor newer colleagues and run discovery for larger accounts. This progression frequently points to AI Solutions Architect roles, which concentrate on designing end-to-end enterprise AI platforms, balancing cost, security and reliability, and advising senior leaders on technology direction.

What an AI Forward Deployed Engineer is paid varies with experience, the kind of employer and the breadth of the technical work. As many AI vacancies are advertised across India or for remote work, learners in Rajkot are usually assessed against national benchmarks. The table gives indicative ranges per role, from entry-level to architect-level responsibility. Take them as market background, not as results promised by any training institute.

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 sources each handle salary data in their own way, with some based on employee submissions and others on job postings or published reporting, which is why we weigh them against one another instead of relying on a lone figure. The ranges are rounded into bands because real offers depend on skills, interview outcomes, employer budgets and negotiation. Learners are encouraged to treat the table as a tool for setting sensible expectations and anticipating how pay may grow.

Uncodemy's hiring network covers 850+ companies, and a number of sectors of interest to Rajkot learners recruit for applied AI and deployment skills. The ones below match this program's content most closely.

• Enterprise software and SaaS: Product businesses embedding RAG assistants and agents in their software want engineers who release dependable work.
• IT services: Service firms create AI solutions for clients abroad and prize engineers able to build and to handle customer conversations.
• Fintech: Financial platforms use AI for documents and customer support, where security, guardrails and data privacy are of great importance.
• Product engineering: Teams developing AI-enabled products want people who can join APIs, pipelines and cloud deployment.
• Consulting and system integrators: Consultancies look for professionals able to lead discovery, shape solutions and deliver production systems.

What employers prioritise differs, so align your portfolio with the sector you aim for.

The path to AI Forward Deployed Engineer runs from technical basics to client-ready deployment in clear stages. The seven steps below follow this course's sequence, each one laying the groundwork for what comes after.

1. Start with FDE and enterprise AI fundamentals: Understand the role, how delivery proceeds and how business problems become AI projects.
2. Gain Python, SQL, Git and API engineering ability: Set up the coding foundation needed for data work, version control and FastAPI services.
3. Explore LLM fundamentals and generative AI: Find out how large language models function and how to apply them in real products.
4. Sharpen prompt and context engineering: Write reliable prompts and manage context so that model responses remain accurate and steady.
5. Develop RAG and enterprise knowledge systems: Construct retrieval pipelines on vector databases that tie answers to company data.
6. Master AI agents, tool calling and MCP: Build agents that operate tools and connect to systems via the Model Context Protocol.
7. Complete cloud deployment, evaluation and the capstone: Release with Docker and CI/CD, measure results, and present an enterprise AI project.

Timeline: [CONFIRM – duration pending; do not state a specific month count until confirmed by Admin / Mr. Irshad.]

Which AI course suits you depends on where you want your career to go. A table of this program's main attributes appears below, and the paragraph after it offers an honest comparison with Uncodemy's other AI courses to guide your decision.

When placed next to Uncodemy's LLM Engineering and RAG Engineering courses, this AI Forward Deployed Engineer program is the wider, client-facing enterprise-deployment option. It folds discovery, RAG, agents, MCP, evaluation, deployment and consulting into a single track. LLM Engineering and RAG Engineering are narrower, deeper-technical specializations that suit learners who already know which layer of the stack they want to go deep on. Take this program if you aim to work with enterprise clients and own delivery from problem to production; take one of the others if you want depth in one technical area.

Rajkot learners go through this program fully live online, with the same trainers, curriculum and batches as Uncodemy's classroom programs. Sessions are scheduled and held in real time, so you can ask trainers questions directly, practise alongside classmates and join discussions, instead of passively watching recorded lectures. Studying online spares you the daily commute, which works well for professionals who can only attend after work or on weekends, and allows you to learn from your own desk with your own equipment. With weekday and weekend batches, it is easier to balance the course with employment or college. Recordings are provided for revision.

For learners who prefer to be in a room together and are able to travel, classroom training continues to be available at Uncodemy's Delhi and Noida centres. Uncodemy has no centre in Rajkot, so live online is the primary mode here, and the quality of teaching, fees and certificate are the same in both formats.

The course duration is 5-6 months. It combines 177 hours of learning, 13 modules and 100+ live sessions. Weekday and weekend batches allow you to select a pace that works with your job or education.

Basic programming familiarity is useful, but advanced coding skill is not compulsory [CONFIRM exact requirement]. Module 2 covers Python, SQL, Git and API engineering, so the coding groundwork is laid early, ahead of LLMs, RAG and agents.

The course includes 20+ tools and technologies: 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, both are core topics. Module 6 deals with RAG and enterprise knowledge systems, and Module 7 with AI agents, tool calling and MCP. Each module spans 15 hours and involves hands-on work with real frameworks.

The capstone is the Enterprise AI Deployment & Capstone Project in Module 13, which takes 25 hours. You plan, evaluate and deploy an enterprise-style AI solution using RAG, agents, guardrails, observability and CI/CD, and then present it in the manner of a client handover.

The classes are live. There are 100+ live sessions run by trainers, where you can ask questions as they arise. Recordings are available for revision, useful for catching up on a missed session or reviewing a topic.

The program fee is ₹19,500 + 18% GST, bringing the total to ₹23,010. This fee holds across every mode and city, whether you attend live online or a classroom batch in Delhi or Noida, and no hidden charges apply.

Yes, EMI starts at ₹3,835 a month for 6 months*, which equals the ₹23,010 total. EMI amount, tenure and eligibility may vary depending on the applicable payment or financing option.

Please reach out to Uncodemy to learn whether a demo class is offered for upcoming batches. The team can share timings and help you decide if the program is right for you. Call +91 9818366550 or +91 8766313646, or write to info@uncodemy.com.

Yes, Uncodemy gives Dedicated Career & Placement Assistance, backed by a hiring network of 850+ companies. It covers resume and LinkedIn support, mock interviews and sharing of job openings. It is assistance only and does not guarantee employment.

The course prepares you to apply for entry-level roles, which are indicatively paid ₹6–10 LPA in the market. Results depend on your skills, portfolio, interview performance and the employer, and neither a job nor a specific salary is guaranteed.

Indicative ranges go from ₹6–10 LPA at entry level up to ₹20–35 LPA for AI Solutions Architects. Current India reporting places FDE salaries broadly around ₹9–27 LPA (source: Express Computer). These are indicative market ranges, not guaranteed outcomes.

You can target Forward Deployed Engineer, AI Solutions Engineer, Applied AI Engineer, AI Integration Engineer and Entry-Level AI Forward Deployed Engineer roles. With experience, you can progress to Senior Forward Deployed Engineer and AI Solutions Architect.

The course is suited to software and IT professionals, freshers with programming fundamentals, data and ML engineers, career changers and working professionals. You need a laptop and a dependable internet connection for live online sessions.

No. Salary ranges are indicative market ranges, and review counts and ratings reflect publicly available platform information that may change. Career and placement assistance does not constitute a guarantee of employment or a specific salary package.

No, Uncodemy does not have a centre in Rajkot. Rajkot learners join live online. Classroom centres are located in Delhi (Laxmi Nagar/Shakarpur) and Noida (Sector 1) for those who can travel.

This course is the wider, client-facing enterprise-deployment program covering discovery, RAG, agents, MCP, evaluation and deployment. LLM Engineering and RAG Engineering are narrower, deeper-technical specializations, as compared in the guide above.