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.