Lead Data Scientist

Job Summary

We are looking for a highly skilled Lead Data Scientist with strong expertise in Agentic AI, Generative AI, Machine Learning, Deep Learning, NLP, RAG architectures, Azure AI ecosystem, and Statistical Modeling. The ideal candidate will lead the design and deployment of enterprise AI solutions, build advanced AI agents, develop LLM-powered applications, and drive data science initiatives from ideation to production.

This role requires a balance of hands-on technical expertise, problem-solving ability, and leadership in delivering business-impacting AI solutions.

Key Responsibilities

Agentic AI & Generative AI

• Design and develop AI agents and multi-agent systems using:
• LangGraph
• Semantic Kernel
• LangChain
• Develop Machine learning models, MMM models, and good in Analytical thoughts
• Very good at SQL coding as well, Knowledge in Snowflake is add on.
• Implement tool calling, reasoning workflows, memory management, and agent orchestration.
• Evaluate and optimize agent performance, grounding, accuracy, and reliability.

Retrieval-Augmented Generation (RAG)

• Design and implement enterprise-grade RAG solutions.
• Build knowledge retrieval pipelines using:
• Azure AI Search
• Vector Databases
• Embedding Models
• Optimize chunking, indexing, retrieval, reranking, and prompt engineering strategies.
• Experience with Graph RAG and Agentic RAG is preferred.

Machine Learning & Deep Learning

• Can write production grade SQL
• Build and deploy models for:
• Classification
• Regression
• Forecasting
• Recommendation Systems
• Anomaly Detection
• Develop deep learning solutions using:
• PyTorch
• TensorFlow
• Keras
• Perform feature engineering, model tuning, validation, and performance optimization.

NLP & LLMs

• Develop NLP solutions involving:
• Text Classification
• Entity Extraction
• Semantic Search
• Conversational AI
• Sentiment Analysis

Statistics & Analytics

Strong understanding of:

• Probability & Statistics
• Hypothesis Testing
• A/B Testing
• Regression Analysis
• Bayesian Statistics
• Time Series Forecasting
• Experimental Design
• Statistical Inference
• Feature Selection Techniques

MLOps / LLMOps

• Build and deploy scalable AI solutions.
• Create CI/CD pipelines for ML models.
• Implement model monitoring, evaluation, and governance.
• Experience with:
• MLflow
• Docker
• Kubernetes
• GitHub Actions
• Azure DevOps

Experience

• 6–9 years of experience in Data Science and Machine Learning.
• Hands on SQL pipelines for deployment
• 3+ years of experience working on Generative AI solutions.
• Hands-on experience building RAG and LLM-based applications.
• Experience deploying AI solutions on Azure Cloud.
• Experience mentoring junior data scientists and leading technical initiatives.

Nice to Have

• Knowledge of GraphRAG.
• Fine-tuning open-source LLMs.
• Knowledge of Responsible AI and AI Governance.
• Azure AI Engineer Certification.
• Experience building enterprise AI Copilots.