Data Scientist

Experience: 3–5 Years 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. Show more