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

- Solid 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

📌 Data Scientist (Gurugram)
🏢 Neurealm
📍 Gurugram