AI Forward Deployed Engineer Training Course in Kurnool (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

For Kurnool learners, the career scope of an AI Forward Deployed Engineer is widening, because employers want people who can turn AI proposals into reliable, client-ready solutions. Such jobs are project-oriented and frequently open to remote work, so learners in and around Kurnool can pursue openings with IT-services firms, enterprise software companies and product-engineering teams across India without moving cities. As enterprises graduate from small AI trials to full production rollouts, they need engineers who pair LLM knowledge with data-integration experience and confident client communication.

Hiring managers typically trust demonstrated work over credentials alone: a functioning RAG system, an agent that calls real tools, and a deployment that is measured and monitored. Interviews generally put the most weight on three things. One is RAG and agent system design, including how you select retrieval approaches and deal with failures. Another is deployment experience using Docker, cloud services and CI/CD. The third is client-facing problem solving, such as untangling vague requirements and explaining trade-offs to non-technical stakeholders. This course is organised around those precise expectations.

Completing the program prepares you for jobs that connect AI engineering with client delivery. Since the syllabus spans discovery, RAG, agents, MCP, evaluation and deployment, graduates can pursue a cluster of related positions. The table names each role and the core tools it leans on, listed from entry level through to senior progression.

Over a span of two to five years, many engineers grow from delivering individual solutions into running whole client engagements. They take responsibility for architecture choices, coach less experienced teammates and lead discovery on larger accounts. That trajectory commonly leads to AI Solutions Architect roles, where the work centres on designing complete enterprise AI platforms, balancing cost with security and reliability, and guiding leadership on technology direction.

What an AI Forward Deployed Engineer earns depends on experience, the type of employer and how broad the technical scope is. Since many AI openings are posted nationally or for remote work, learners in Kurnool are generally compared with India-wide benchmarks. The table lists indicative ranges for each role, from entry-level jobs to architect-level responsibility. Use them as background on the market, not as outcomes pledged by any training provider.

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. Each of these reports pay differently: some depend on figures submitted by employees, others on job advertisements or journalism, so we compare them instead of leaning on a single number. The bands shown are rounded, as actual offers turn on skills, interview results, employer budgets and negotiation. Learners should treat this table as a guide for setting realistic expectations and thinking about how their pay might develop over time.

Uncodemy's hiring network spans 850+ companies, and several sectors that matter to Kurnool learners hire for applied AI and deployment skills. The sectors below align most closely with what this program teaches.

• Enterprise software and SaaS: Product companies adding RAG assistants and agents to business tools need engineers who deliver dependably.
• IT services: Service providers build AI solutions for international clients and value engineers who can both build and communicate with customers.
• Fintech: Financial platforms apply AI to documents and support, where security, guardrails and data privacy carry great weight.
• Product engineering: Teams creating AI-enabled products want people able to combine APIs, pipelines and cloud deployment.
• Consulting and system integrators: Consultancies need professionals who can lead discovery, shape solutions and hand over production systems.

Employer priorities differ, so tailor your portfolio to the sector you are targeting.

Becoming an AI Forward Deployed Engineer is a staged path from technical groundwork to client-ready deployment. The seven steps below mirror the order of this course, with every skill preparing the ground for the next one.

1. Grasp FDE and enterprise AI fundamentals: Learn what the role involves, how delivery works and how business problems are framed as AI initiatives.
2. Strengthen Python, SQL, Git and API engineering: Establish the coding base for handling data, managing versions and building FastAPI services.
3. Understand LLM fundamentals and generative AI: See how large language models operate and how to put them to use in applications.
4. Refine prompt and context engineering: Craft dependable prompts and control context so model answers stay accurate and consistent.
5. Create RAG and enterprise knowledge systems: Assemble retrieval pipelines on vector databases that root answers in company data.
6. Build AI agents, tool calling and MCP skills: Develop agents that use tools and connect to systems through the Model Context Protocol.
7. Close with cloud deployment, evaluation and the capstone: Ship with Docker and CI/CD, assess performance, and present an enterprise AI project.

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

The best AI course is the one that fits the career you want. Below, a table lists this program's key attributes, and the paragraph that follows compares it candidly with Uncodemy's other AI courses so you can decide.

Compared with Uncodemy's LLM Engineering and RAG Engineering courses, this AI Forward Deployed Engineer program is the broader, client-facing enterprise-deployment route. It gathers discovery, RAG, agents, MCP, evaluation, deployment and consulting into one journey. LLM Engineering and RAG Engineering are narrower, deeper-technical specializations, a better match for learners who already know which part of the stack they want to master. Choose this program if you want to work alongside enterprise clients and own delivery from problem to production; choose one of the others if you want depth in a single technical area.

Kurnool learners complete this program entirely live online, taught by the same trainers, following the same curriculum and joining the same batches as Uncodemy's classroom programs. You attend scheduled sessions in real time, put questions directly to trainers, work through exercises as a group and interact with other learners, rather than watching pre-recorded lessons on your own.

Learning online removes the daily commute, which helps working professionals who can study only in the evenings or on weekends, and lets you learn at your own desk with your own setup. Weekday and weekend batches make it simpler to fit the course around a job or college timetable. Recordings are available for revision.

Anyone who prefers face-to-face learning and can travel still has classroom training at Uncodemy's Delhi and Noida centres. Because Uncodemy has no centre in Kurnool, live online is the main route here, and the teaching quality, fees and certificate remain the same in either format.

The program lasts 5-6 months. It brings together 177 hours of learning, 13 modules and 100+ live sessions. With weekday and weekend batches on offer, you can choose a rhythm that suits your job or studies.

A basic knowledge of programming is useful, though advanced prior coding is not required [CONFIRM exact requirement]. Module 2 covers Python, SQL, Git and API engineering, so the coding foundation is laid early, before you reach LLMs, RAG and agents.

The course spans 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, and both sit at the heart of the curriculum. Module 6 teaches RAG and enterprise knowledge systems, and Module 7 teaches AI agents, tool calling and MCP. Each lasts 15 hours and features practical work with real frameworks.

The capstone is the Enterprise AI Deployment & Capstone Project in Module 13, lasting 25 hours. You design, evaluate and deploy an enterprise-style AI solution that uses RAG, agents, guardrails, observability and CI/CD, then present it as you would to a client.

The classes are live. You join 100+ live sessions led by trainers and can raise questions as they happen. Recordings are supplied for revision, handy if you miss a class or wish to revisit a topic.

The program fee is ₹19,500 + 18% GST, giving a total of ₹23,010. The same fee applies in every mode and city, whether you learn live online or attend a classroom batch in Delhi or Noida, and there are no hidden charges.

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

Please get in touch with Uncodemy to ask whether a demo class is available for upcoming batches. The team can share the schedule and help you judge whether the program fits. Call +91 9818366550 or +91 8766313646, or email info@uncodemy.com.

Yes, Uncodemy provides Dedicated Career & Placement Assistance, supported by a hiring network of 850+ companies. Help includes resume and LinkedIn guidance, mock interviews and sharing of openings. It is assistance only and does not guarantee employment.

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

Indicative ranges stretch from ₹6–10 LPA at entry level 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 and not guaranteed outcomes.

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

The course is meant for software and IT professionals, freshers with programming fundamentals, data and ML engineers, career changers and working professionals. A laptop and a stable internet connection are needed for the live online sessions.

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

No, Uncodemy has no centre in Kurnool. Kurnool learners join live online. The classroom centres are in Delhi (Laxmi Nagar/Shakarpur) and Noida (Sector 1) for those able to travel.

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