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

• FDE role, responsibilities and delivery lifecycle
• Enterprise AI use cases and business value framing
• AI project stages from discovery to production
• Stakeholder communication and requirement gathering basics
• Enterprise AI architecture overview

• Python for AI engineering: data structures, functions, modules
• SQL queries, joins and data retrieval
• Git and GitHub workflows, branching, pull requests
• REST APIs and FastAPI service development
• Error handling, testing and JSON data handling

• Transformer and LLM concepts, tokens and context windows
• OpenAI APIs and Hugging Face models
• Generative AI application patterns
• Model parameters, cost and latency considerations
• Limitations, hallucinations and responsible usage

• Prompt design patterns and structured outputs
• System prompts, few-shot and chain-of-thought techniques
• Context engineering and context window management
• Prompt testing and versioning
• Reducing hallucination through context design

• Ingesting data from documents, databases and APIs
• Data cleaning, chunking and transformation
• PostgreSQL and pgvector fundamentals
• Building repeatable AI data pipelines
• Enterprise data access, permissions and governance basics

• RAG architecture and embeddings
• Vector databases and similarity search
• LangChain and LlamaIndex retrieval pipelines
• Chunking strategies, reranking and hybrid retrieval
• Enterprise knowledge assistants and RAG evaluation with RAGAS

• AI agent concepts, planning and memory
• Tool calling and function calling with APIs
• LangGraph workflows and multi-step agents
• Model Context Protocol (MCP) servers and clients
• Human-in-the-loop controls and agent failure handling

• Building AI backends with FastAPI
• Integrating OpenAI APIs and LangChain components
• Session handling, streaming responses and caching
• Front-end integration basics for AI applications
• Application structure, configuration and secrets management

• Evaluation metrics for LLM and RAG applications
• RAGAS-based test sets and scoring
• Tracing and monitoring with LangSmith/Langfuse
• Latency, cost and quality dashboards
• Regression testing and reliability practices

• Prompt injection and jailbreak risks
• Guardrails, input/output validation and content filtering
• Data privacy, PII handling and access control
• Secure API key and secrets practices
• Safe tool and agent permissions in enterprise settings

• Containerising AI services with Docker
• AWS/Azure deployment fundamentals
• CI/CD pipelines with GitHub
• Environment configuration, scaling and rollbacks
• Post-deployment monitoring and logging

• Customer discovery interviews and problem scoping
• Translating business needs into AI solution architecture
• Feasibility, risk and ROI assessment
• Client demos, documentation and handover
• Client case study analysis

• Capstone scoping from a real-style client brief
• End-to-end build: data, RAG, agents, MCP
• Evaluation, guardrails and observability integration
• Cloud deployment with Docker and CI/CD
• Final presentation, review and feedback

AI Forward Deployed Engineer roles are open to Amravati learners mainly through remote, hybrid and relocation-based openings, since most of this hiring sits with larger IT and product-engineering employers. Amravati's own IT-services and enterprise-software activity is smaller than that of metros, but companies of all sizes are adopting applied AI, and they need engineers who can connect models to real business data. Learners can also look toward established hubs such as Hinjewadi, Kharadi and Magarpatta in Pune, where product and enterprise-software teams hire for applied AI work, and toward other Maharashtra cities. Uncodemy's 850+ Companies in Our Hiring Network span India, so career assistance is not tied to one city.

Hiring managers for these roles tend to weigh three things most: RAG and agent system design, hands-on deployment experience, and the ability to solve problems directly with clients. The capstone project is built to give you evidence for all three. Outcomes vary by learner, skills, experience, location and employer.

This course prepares you for roles where AI engineering meets client delivery. Entry-level positions focus on building and supporting deployments, while mid-level roles take ownership of integrations and solution design. Which role fits depends on your background, interview performance and the employer's needs, so treat the table as a map of possibilities rather than a promise.

Over roughly two to five years, many professionals widen their scope from building individual deployments to designing whole solutions. They take on larger client engagements, make architecture decisions about data, models and security, and mentor junior engineers. That path can lead toward AI Solutions Architect roles, where you shape technical strategy across several projects. Progression speed depends on experience, company and the scope of work you take on.

Salaries for AI Forward Deployed Engineers in India follow role, skills and employer rather than the learner's city alone, so Amravati learners applying to remote or hybrid roles are generally compared against the same market ranges. The table lists indicative bands for seven related roles. Entry-level positions sit at the lower end, while architecture and senior roles reach higher bands as experience and scope grow.

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 were compared to keep the ranges realistic, and no figure on this page promises any individual result. Compensation differs by company size, domain, location, interview performance and prior experience, and it can change as the AI hiring market shifts. Use the ranges as a planning guide, then confirm current numbers with employers and recruiters before making career or financial decisions.

Demand for deployment-focused AI engineers is spread across several sectors, and Uncodemy's 850+ hiring partners include organizations that hire across them. The sectors below are the most relevant for Amravati learners, whether they work remotely, hybrid or on relocation.

• Enterprise software and SaaS: Product companies embedding RAG assistants and agents into their platforms need engineers who can tailor solutions per client.
• IT services: Service firms deliver AI projects for clients and need engineers comfortable with discovery, integration and deployment.
• Fintech: Financial technology teams use applied AI for document processing and support, with strong security and guardrail requirements.
• Product engineering: Engineering-led companies add AI features to existing products and need people who can ship and monitor them.
• Consulting and system integrators: Consultancies run client-facing AI programs that reward both technical depth and clear communication.

Sector demand changes over time, so check current openings regularly and match your portfolio to the roles you target.

Becoming an FDE is a sequence of skills that build on one another. The steps below follow the course path, from understanding the role to delivering a deployed, evaluated solution for a realistic client brief.

1. Learn FDE and enterprise AI fundamentals: Understand the role, the delivery lifecycle and how business problems translate into AI projects.
2. Master Python, SQL, Git and API engineering: Build the coding base needed for data access, version control and service development.
3. Study LLM fundamentals and generative AI: Learn how large language models work, their limits and how to call them through APIs.
4. Practise prompt and context engineering: Design prompts and context that produce reliable, structured and testable model behaviour.
5. Build RAG and enterprise knowledge systems: Create retrieval pipelines with embeddings and vector databases over real organisational data.
6. Develop AI agents, tool calling and MCP skills: Build multi-step agents that call tools and connect to systems through MCP.
7. Finish with cloud deployment, evaluation and a capstone: Deploy with Docker and CI/CD, measure quality, and complete an enterprise project.

Timeline: The program runs for 5-6 months.

Choosing between AI courses is easier when you compare the same factors side by side. The table summarises this program's key facts, and the paragraph after it explains how it differs from Uncodemy's own LLM Engineering and RAG Engineering courses.

This AI Forward Deployed Engineer course is the broader, client-facing enterprise-deployment program. It covers customer discovery, RAG, agents, MCP, evaluation, deployment and consulting in a single path. Uncodemy's LLM Engineering and RAG Engineering courses are narrower, deeper-technical specializations that focus on fewer topics in more depth. If you want to work with enterprise clients and own delivery end to end, this program fits better. If you want to specialise in one technical area, the focused courses may suit you more.

Amravati learners join this program fully live online, with the same trainers, curriculum and batches that Uncodemy uses for its classroom programs. You attend scheduled sessions in real time, ask questions as they come up, take part in labs and assignments, and work on projects with trainer guidance, so the experience is interactive rather than a collection of pre-recorded videos.

Studying from home also saves travel time and cost, which lets working professionals and students fit learning around their routines. Weekday and weekend batches are available, with IST timings.

In-person classroom training remains available at Uncodemy's Delhi and Noida centres for those who can travel and prefer a physical setting. Uncodemy does not operate a classroom in Amravati, and this page does not suggest otherwise. The fee is the same across modes and cities, so the choice depends on your preference and circumstances.

The program runs for 5-6 months. It includes 177 hours of learning, 100+ live sessions and 13 modules, delivered live online for Amravati learners in weekday and weekend batches.

Basic programming or Python familiarity helps, but deep coding experience is not mandatory. Module 2 teaches Python, SQL, Git and API engineering, so you build these skills early.

The course covers 20+ tools and technologies. These include 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 parts of the curriculum. Module 6 covers RAG and enterprise knowledge systems for 15 hours, and Module 7 covers AI agents, tool calling and MCP for another 15 hours.

The capstone is the Enterprise AI Deployment & Capstone Project, which takes 25 hours. You build and deploy an end-to-end enterprise AI solution, bringing together RAG, agents, MCP, evaluation, guardrails and cloud deployment, then present it for review.

Classes are live, with 100+ live sessions led by trainers in real time. Recordings are available for revision, so you can revisit topics after class, though they supplement live attendance rather than replace it.

The program fee is ₹19,500 + 18% GST, which totals ₹23,010. The fee is the same in all modes and cities, with no hidden charges, and EMI options are available.

Yes, EMI starts from ₹3,835 per month for 6 months, which adds up to the ₹23,010 total. EMI amount, tenure and eligibility may vary depending on the applicable payment or financing option.

Contact the admissions team on +91 9818366550 / +91 8766313646 or at info@uncodemy.com to ask about demo availability before you enrol, and to get your questions about batches answered.

Yes, Uncodemy provides Dedicated Career & Placement Assistance, supported by a network of 850+ hiring partners. This includes resume and LinkedIn help, mock interviews and opportunity sharing, but it is not a guarantee of employment.

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

Salaries vary by role, ranging from ₹6–10 LPA at entry level to ₹20–35 LPA for AI Solutions Architects. Current India reporting places FDE salaries around ₹9–27 LPA (source: Express Computer). Ranges are indicative market ranges, not guaranteed placement 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, Senior Forward Deployed Engineer and AI Solutions Architect paths open up.

Software and IT professionals, freshers with programming fundamentals, data and ML engineers, career changers from technical consulting, and working professionals can join. You need a laptop and stable internet for live online sessions.

No. Statistics are based on Uncodemy's internal records and public review platforms. Review counts and ratings may change, and placement and salary outcomes vary by learner, skills, experience, location, employer and market conditions.

No, Uncodemy has no classroom centre in Amravati. Amravati learners join live online, while classroom centres are in Delhi (Laxmi Nagar/Shakarpur) and Noida (Sector 1) for those who 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 specializations focused on fewer topics.