AI Solutions Engineer
Course in Hyderabad

1. 1

   Python Backend Development with AI

   Module 2 · 40 Hrs

   Solutions rest on working APIs and data stores. Concentrate on FastAPI design, PostgreSQL, authentication and access control and cloud deployment, since your prototypes and integrations depend on them.

   What you study

   • FastAPI development and RESTful API design
   • PostgreSQL and MongoDB database fundamentals
   • Authentication, authorization and JWT / OAuth
   • API security and role-based access control
   • Docker containerization
   • Cloud deployment fundamentals

   Tools you use

   FastAPIPostgreSQLDockerAWS

   Hands-on lab

   Deploy a production-style backend service to a cloud platform.

   See the full module →
2. 2

   Generative AI & LLM Application Engineering

   Module 4 · 40 Hrs

   Most client requests today involve documents and language models. Learn LLM APIs, structured outputs, embeddings and RAG well enough to judge what is feasible and to prototype it quickly.

   What you study

   • Large language models
   • LLM APIs and prompt engineering
   • Structured outputs and function calling
   • Embeddings and vector databases
   • Retrieval-Augmented Generation (RAG)
   • Reranking and RAG evaluation

   Tools you use

   LLM APIspgvectorLangChainFastAPI

   Hands-on project

   Generative AI / RAG Application. Build LLM-powered apps with RAG, vector search and document understanding.

   See the full module →
3. 3

   Agentic AI Engineering

   Module 5 · 40 Hrs

   Clients also ask for assistants that act. Focus on tool calling, MCP, human-in-the-loop design and guardrails, so you can explain honestly what an agent can safely do inside a business process.

   What you study

   • AI agent fundamentals and architecture
   • Tool calling and function calling
   • Model Context Protocol (MCP)
   • Multi-agent patterns
   • Human-in-the-loop design
   • Retries, fallbacks and guardrails

   Tools you use

   LangGraphMCPLLM APIsPostgreSQL

   Hands-on lab

   Implement multi-step agent workflows with routing and human-in-the-loop checkpoints.

   See the full module →
4. 4

   AI Solutions Engineering

   Module 7 · 40 Hrs

   This module is the centre of the role. Give it the most time: discovery, workflow mapping, architecture, integration, PII handling, UAT, cost estimation, documentation and client demonstrations.

   What you study

   • Client discovery and requirement gathering
   • Workflow mapping
   • AI solution architecture
   • Data and system integration
   • Security and PII handling
   • UAT and deployment
   • Architecture diagrams and client demonstrations

   Tools you use

   PythonFastAPIREST APIsCloud PlatformGitHub

   Hands-on project

   End-to-End AI Solution (Capstone). Design and build a complete AI solution from requirements to deployment and live demonstration.

   See the full module →
5. 5

   MLOps, LLMOps & AI Platform Engineering

   Module 6 · 30 Hrs

   Clients care about running cost, security and reliability. Concentrate on evaluation, cost and latency management, cloud access rights, and AI security, so your designs include the operating plan.

   What you study

   • AI and RAG evaluation
   • CI/CD for AI systems
   • Cloud IAM and secrets management
   • Scaling and rollbacks
   • Cost and latency management
   • AI security and reliability

   Tools you use

   MLflowGitHub ActionsCloud PlatformMonitoring Tools

   Hands-on lab

   Manage cloud IAM, secrets, scaling and rollback strategy for a deployed AI platform.

   See the full module →

What the programme covers for this role. The programme teaches solution delivery as part of an AI engineering course: discovery, architecture, integration, UAT and demos. It is not a full consulting or pre-sales programme, so sales skills and industry domain knowledge are built through practice on the job.