Data Scientist

Key Responsibilities

• Design, develop, and deploy Machine Learning and Generative AI solutions.
• Build Retrieval-Augmented Generation (RAG) pipelines using vector databases and enterprise knowledge sources.
• Develop AI agents using Agentic AI frameworks such as LangGraph, LangChain, CrewAI, or similar technologies.
• Integrate AI agents with enterprise APIs, tools, databases, and external services.
• Develop prompts, tool-calling workflows, and structured output pipelines for LLM applications.
• Fine-tune, evaluate, and optimize LLM-powered applications for accuracy, latency, and cost.
• Implement data preprocessing, feature engineering, and ML model training workflows.
• Work with structured and unstructured datasets to solve business problems.
• Collaborate with Product Managers, Software Engineers, and Subject Matter Experts to deliver AI-driven features.
• Monitor model and agent performance and participate in troubleshooting and continuous improvements.
• Write clean, maintainable, and well-tested Python code following engineering best practices.
• Stay updated with the latest advancements in Machine Learning, LLMs, and Agentic AI technologies.

Required Technical Skills

Core Skills

• Strong proficiency in Python
• Machine Learning fundamentals
• Natural Language Processing (NLP)
• Generative AI and Large Language Models (LLMs)
• Prompt Engineering
• Retrieval-Augmented Generation (RAG)
• Embeddings and semantic search
• Model evaluation and validation techniques

Agentic AI Frameworks

• Hands-on experience with LangChain and LangGraph
• Experience building AI agents with tool calling and workflow orchestration
• Familiarity with CrewAI, AutoGen, Semantic Kernel, or similar frameworks
• Understanding of agent memory, planning, state management, and multi-step reasoning

ML & AI Libraries

• Scikit-learn
• XGBoost or LightGBM
• PyTorch or TensorFlow
• Hugging Face Transformers
• OpenAI, Anthropic, Gemini, Bedrock, Azure OpenAI, or similar LLM APIs
• Vector databases such as Pinecone, FAISS, ChromaDB, Weaviate, Milvus, or OpenSearch

Data & Cloud

• SQL and relational databases
• Experience with AWS, Azure, or GCP
• Docker and containerized deployments
• Basic CI/CD knowledge
• MLflow or similar experiment tracking tools
• REST APIs/FastAPI for AI model deployment

Good to Have

• Experience building production-ready AI or LLM applications.
• Exposure to multi-agent systems and workflow orchestration.
• Knowledge of Model Context Protocol (MCP).
• Experience with AI evaluation frameworks and guardrails.
• Understanding of MLOps and model monitoring.
• Experience with fine-tuning techniques such as LoRA, PEFT, or QLoRA.
• Experience with document processing, OCR, or document intelligence.
• Experience in legal, regulatory, financial, healthcare, or publishing domains