Hemanth Kaleru
Generative AI Engineer • Hyderabad, India • k**************@gmail.com • +91*******876 • linkedin.com/••••• • drivetube.ai/•••••
Professional Summary
Generative AI Engineer with 1.5+ years of experience building enterprise Generative AI applications, RAG systems, and production-ready AI assistants. Experienced in LangChain, LangGraph, FastAPI, ChromaDB, PostgreSQL, Docker, Kubernetes, and integrating LLM providers (OpenAI, Groq, Gemini) to deliver scalable semantic search, agentic workflows, and automation solutions.
Technical Skills
Programming Languages: Python
Web Technologies: REST APIs
Frameworks and Libraries: LangChain,LangGraph,Multi-Agent Systems,Tool Calling,FastAPI,Hugging Face
Databases: PostgreSQL,ChromaDB,Vector Databases,Embeddings,Redis,Airtable
Cloud and DevOps: Docker,Docker Compose,Kubernetes,Hugging Face Spaces
Tools and Methodologies: Git,GitHub,OpenAI API,Groq API,Gemini API,n8n,Gradio,Webhooks,Gmail API,Google Calendar API
AI & LLMs: Generative AI,Large Language Models,Retrieval-Augmented Generation,Agentic AI,Prompt Engineering,LLM Evaluation,Semantic Search,Model Context Protocol MCP
Backend & APIs: Asyncio,Pydantic,Microservices
Retrieval & IR: BM25,Hybrid Retrieval,Reciprocal Rank Fusion
Work Experience
LTIMindtree
Hyderabad, India
Software Engineer
Dec 2024 – Present
Worked at LTIMindtree, an IT services and consulting firm, delivering enterprise AI and backend engineering for automation, retrieval, and conversational applications.
Tech Stack: Python, FastAPI, Pydantic, Asyncio, LangChain, LangGraph, ChromaDB, PostgreSQL, OpenAI API, Groq API, Gemini API, Docker, Kubernetes, Redis, Git, GitHub
- Built enterprise Generative AI automation solutions using Python to streamline internal business workflows, reducing manual processing overhead and enabling scalable AI-driven automation.
- Implemented RESTful microservices with FastAPI and Pydantic to serve LLM-backed features and integrations, backed by PostgreSQL for structured data and ChromaDB for vector retrieval.
- Designed and deployed RALMA, an enterprise RAG chatbot using LangChain and ChromaDB for document retrieval, reducing manual query resolution time by 37% through improved knowledge retrieval pipelines.
- Integrated multiple LLM providers (OpenAI, Groq, Gemini) into production pipelines, implementing async Python request handling and streaming responses to support concurrent conversational loads.
- Containerized services with Docker and Kubernetes and collaborated across product and operations teams to automate deployments, improving operational efficiency by ~18%.
- Engineered hybrid retrieval strategies (ChromaDB embeddings + BM25 fusion), added monitoring and testable APIs, and maintained Git-based source control to ensure reliable rollout of AI features.
Projects
Hemanth AI Assistant — RAG-Based Portfolio Chatbot
Tools Used: Python, LangChain, ChromaDB, OpenAI API, Gradio, Hugging Face Spaces
- Built a production RAG chatbot that ingests documents, creates embeddings, and performs vector retrieval to answer portfolio-related queries.
- Implemented intelligent query routing and a Gradio UI and deployed the assistant on Hugging Face Spaces for public access and demonstration.
AI Resume Screening & Interview Scheduling Automation (n8n)
Tools Used: n8n, Python, PostgreSQL, OpenAI API, Gmail API, Google Calendar API
- Designed an end-to-end recruitment workflow using n8n to automate resume parsing, ATS scoring, candidate evaluation, and interview scheduling.
- Integrated AI-based resume parsing, PostgreSQL storage, email notifications, and human approval gates to streamline hiring operations.
Document-to-Architecture RAG System — Enterprise Knowledge Assistant
Tools Used: Python, FastAPI, ChromaDB, BM25, Pydantic, OpenAI API, Groq API, Gemini API
- Built a FastAPI-based RAG system that ingests PDF/DOCX/TXT/MD files and auto-generates structured technical architecture designs with Mermaid diagrams.
- Implemented hybrid retrieval (ChromaDB + BM25 via Reciprocal Rank Fusion) and Pydantic-validated structured outputs to ensure consistent architecture generation.
AI Job Search Agent — Multi-Agent Agentic Pipeline
Tools Used: Python, FastAPI, LangGraph, LangChain, ChromaDB, Groq API, Gemini API
- Designed a LangGraph multi-node agentic workflow paired with a FastAPI backend and ChromaDB vector memory to perform semantic job matching.
- Integrated multi-provider LLM support with streaming responses and orchestrated multi-agent tasks for candidate-job alignment.
Education
CMR Institute of Technology, Hyderabad
B. Tech – Computer Science Engineering • Hyderabad, India • 2024
Certifications
Generative AI Foundation Course — LTIMindtree Shoshin School
Database and SQL — Infosys Springboard
AWS Academy Graduate — Cloud Foundations — AWS Academy
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