Experience:

6–12 Years

Key Responsibilities

- Design, develop, and deploy machine learning and GenAI solutions in production environments

- Build and optimize Retrieval-Augmented Generation (RAG) pipelines

- Design, develop, and orchestrate AI agents using modern Agentic AI frameworks to automate complex business workflows.

- Develop multi-agent systems with planning, reasoning, tool usage, and workflow orchestration capabilities.

- Fine-tune and evaluate Large Language Models (LLMs)

- Develop prompt engineering strategies and evaluation frameworks

- Implement scalable ML pipelines using Python

- Work with structured and unstructured data sources

- Collaborate with Engineering, Product, and SMEs to deliver AI-driven features

- Monitor model performance, drift, and reliability in production

- Conduct experimentation, A/B testing, and performance benchmarking

- Contribute to architecture design for AI-powered systems

Required Technical Skills

Core Stack

- Python (advanced proficiency)

- Machine Learning (supervised/unsupervised learning, NLP)

- Generative AI (LLMs, prompt engineering, embeddings)

- RAG architecture and vector search

- Agentic AI concepts including planning, memory, tool calling,



workflow orchestration, and multi-agent collaboration

- Model evaluation and validation frameworks

ML & AI Tools

- Experience with Agentic AI frameworks such as CrewAI, AutoGen, Semantic Kernel, or similar

- Experience building AI agents with tool integration, function calling, and workflow orchestration

- Understanding of agent memory, state management, routing, and human-in-the-loop workflows

- LangChain / LlamaIndex (or similar frameworks)

- Scikit-learn / XGBoost / LightGBM

- PyTorch / TensorFlow

- HuggingFace / OpenAI / LLM APIs

- Vector databases (Pinecone, FAISS, Weaviate, OpenSearch, etc.)

Data & Infrastructure

- SQL and data querying

- Experience with AWS / Azure / GCP

- CI/CD for ML deployments

- Model tracking tools (MLflow preferred)

- REST APIs/FastAPI for AI service deployment

Valuable to Have

- Experience in legal, regulatory, or publishing domains

- Experience with model monitoring and MLOps

- Knowledge of fine-tuning techniques (LoRA, PEFT, QLoRA)

- Experience with multi-agent frameworks (AutoGen, LangGraph, etc.)

📌 Senior Data Scientist (Gurugram)
🏢 Neurealm
📍 Gurugram