Senior Specialist - Data Sciences

Role overview

Senior Specialist - Data Sciences at LTIMindtree

AI Application Development Design, develop, and deploy GenAI and Agentic AI solutions using modern LLM frameworks and tools.

Responsibilities

• Build intelligent AI agents capable of reasoning, planning, tool execution, and workflow automation.
• Develop scalable backend services and APIs for AI applications using Python and FastAPI.
• Translate business requirements into robust AI-powered solutions.
• Agentic AI Development Develop and maintain AI agents using frameworks such as LangChain, LangGraph, AutoGen, CrewAI, or Google ADK.
• Implement agent workflows involving task decomposition, memory management, reasoning, and tool integration.
• Build and support multi-agent collaboration patterns and orchestration workflows.
• Integrate external tools, APIs, databases, and enterprise applications into agent ecosystems.
• Advanced RAG Implementation Design and implement Retrieval-Augmented Generation (RAG) pipelines to improve AI response quality and accuracy.
• Build document ingestion, chunking, embedding generation, retrieval, and response synthesis workflows.
• Implement advanced retrieval techniques including hybrid search, semantic search, metadata filtering, and agentic retrieval.
• Integrate enterprise knowledge repositories and structured/unstructured data sources into AI applications.
• Knowledge & Data Engineering Develop and maintain vector database and semantic search solutions using Pinecone, FAISS, ChromaDB, or other vector stores.
• Work with Knowledge Graphs and metadata-driven architectures to enhance contextual reasoning.
• Implement memory and context management strategies for AI agents.
• AI Evaluation & Observability Develop automated evaluation frameworks for AI applications.
• Measure performance using metrics such as answer relevance, faithfulness, hallucination detection, latency, and user satisfaction.
• Utilize tools such as RAGAS, DeepEval, LangSmith, Langfuse, and Arize AI.
• Monitor production AI systems and proactively identify quality, reliability, and performance issues.
• Engineering Excellence Write clean, scalable, and production-ready code following engineering best practices.
• Participate in code reviews, architecture discussions, and design workshops.
• Support CI/CD implementation and deployment automation for AI solutions.
• Troubleshoot and optimize AI applications for performance, scalability, and reliability.

Required qualifications

• Programming & Development Strong hands-on development experience in Python.
• Experience developing REST APIs and microservices using FastAPI.
• Strong understanding of software engineering best practices, testing frameworks, and API development.
• Agent Frameworks LangChain LangGraph AutoGen CrewAI Google ADK Experience building AI agents and agentic workflows.
• Generative AI & RAG Strong experience implementing Advanced RAG solutions.
• Understanding of embeddings, semantic search, vector retrieval, re-ranking, hybrid search, and prompt engineering.
• Experience integrating enterprise knowledge sources into GenAI applications.
• Multi-Agent Systems Knowledge of multi-agent architectures and agent orchestration patterns.
• Experience implementing tool calling, workflow automation, and agent collaboration mechanisms.
• Vector Databases Pinecone FAISS ChromaDB Weaviate Milvus AI Evaluation & Observability RAGAS DeepEval LangSmith Langfuse Arize AI Understanding of evaluation methodologies for LLM and RAG applications.
• Emerging AI Technologies MCP (Model Context Protocol) Knowledge Graphs Agent Memory Patterns Function Calling and Tool Integration

Employment details

• Location: India

Additional role details

• AI Engineer – Agentic AI & Advanced RAG Exp - 8-12 Years