Lead Data Scientist- Gen AI

Fractal Analytics; Pune District, Bengaluru; On-site; INR 3,500,000 - 6,000,000; Full time

Job summary

Fractal Analytics is seeking a senior AI product leader to own end-to-end AI product development—from data ingestion and model building to deployment, monitoring, and continual improvement in healthcare use cases.

You will design and build AI-driven solutions, optimize GenAI/LLMs, implement agentic AI workflows, and lead multi-disciplinary teams to deliver measurable outcomes with robust observability. Strong Python, ML/DL, and cloud experience are required.

Qualifications

• 7+ years applying computational algorithms and statistical methods to structured and unstructured data.
• 3+ years with AI Cloud services (Azure ML, Databricks, Azure OpenAI) or equivalents on GCP/AWS.
• 1+ year hands-on with LLMs, RAG, and Agentic AI frameworks.
• Experience deploying scalable end-to-end ML/AI solutions.
• Model performance tuning including quantization and optimization.
• Strong Python development and DL frameworks (TensorFlow/PyTorch).
• Subject-matter expertise in text processing, NLU, and SLU.

Responsibilities

• Design & build AI-driven solutions for healthcare challenges.
• Compare and evaluate multiple models and architectures; select best fit.
• Quantize and optimize GenAI/LLM models for performance and cost.
• Implement autonomous, multi-step workflows for healthcare apps.
• Lead large proof-of-concepts spanning on-premises and cloud environments.
• Own full lifecycle: data ingestion, transformation, modeling, deployment, monitoring.

Skills

Python

DL frameworks

LLM & GenAI

NLP/NLU/SLU

Communication

Tools

Langfuse

LangSmith

LangChain

LangGraph

Semantic Kernel

Azure OpenAI

Microsoft AutoGen

Job description

Fractal is a strategic AI partner to Fortune 500 companies with a vision to power every human decision in the enterprise. Fractal is building a world where individual choices, freedom, and diversity are the greatest assets. An ecosystem where human imagination is at the heart of every decision. Where no possibility is written off, only challenged to get better. We believe that a true Fractalite is the one who empowers imagination with intelligence. Fractal has been featured as a Great Place to Work by The Economic Times in partnership with the Great Place to Work Institute and recognized as a Cool Vendor’ and a ‘Vendor to Watch’ by Gartner.

You will lead end-to-end AI product development from data ingestion and model building to deployment, monitoring, and continual improvement—bringing deep expertise in text processing (NLU, SLU), RAG pipelines, and LLMs. You will also compare different models and select the most suitable ones for each solution, ensuring optimal performance and scalability.

What You’ll Do (Responsibilities)

Design & Build AI Solutions

• Develop AI-driven solutions tailored to healthcare business challenges.
• Compare and evaluate multiple models and architectures to select the best fit for each use case.
• Quantize and optimize GenAI/LLM models for performance, cost, and accuracy.
• Implement Agentic AI frameworks to build autonomous, multi-step workflows for healthcare applications.
• Lead large, complex proof-of-concepts spanning on-premises and cloud environments for healthcare use cases.

End-to-End Product Development

• Own the full lifecycle: data ingestion & transformation, feature engineering, analysis & modeling, deployment, testing, performance monitoring, observability, and documentation.
• Build robust RAG pipelines with vector databases for high-recall retrieval and LLM-driven generation.
• Establish, document, and evangelize best practices for machine learning, deep learning, and GenAI development.

LLM Excellence

• Design, implement, and optimize prompts (including In-Context Learning) to improve reliability and task fidelity.
• Integrate Langfuse and LangSmith for LLM observability, tracing, evaluation, and continuous improvement of model behavior in production.

Technical Leadership

• Prioritize and drive technology workstreams for ML, NLP, and GenAI products.
• Mentor and train scientists/engineers on complex AI/ML topics and code quality standards.
• Collaborate with cross-functional stakeholders to translate healthcare business needs into measurable AI outcomes.

Operational Excellence

• Implement monitoring, testing, and maintenance practices to keep models robust, fair, and compliant.
• Manage the lifecycle and governance for a suite of NLP/ML models supporting multiple products.

What You’ll Bring (Qualifications)

• 7+ years applying computational algorithms and statistical methods to structured and unstructured data.
• 3+ years with AI Cloud services (e.g., Azure ML, Databricks, Azure OpenAI), or equivalent on GCP/AWS.
• 1+ year hands-on with LLMs, RAG (Retrieval-Augmented Generation), and Agentic AI frameworks.
• Significant experience deploying scalable solutions end-to-end—from problem definition to successful product launch.
• Practical experience in model performance tuning (latency, throughput, accuracy) including quantization and optimization.
• Strong Python development; expertise in machine learning and deep learning with TensorFlow or PyTorch (including distributed training).
• Deep subject-matter expertise in text processing, NLU, and SLU.
• Proven ability to design effective prompts and optimize them for task performance; experience applying ICL.
• Data engineering fundamentals: ingestion, transformation, and management.
• Mandatory: Hands-on experience with Agentic AI frameworks such as Microsoft AutoGen, LangChain, LangGraph, and Semantic Kernel.
• Excellent communication, writing, and presentation skills.
• Ability to decompose complex problems into clear, implementable solutions and lead cross-functional delivery.

Tools & Technologies

• Programming & DL: Python, TensorFlow, PyTorch, Hugging Face Transformers
• LLM Observability & Evaluation: Langfuse, LangSmith
• LLM & Orchestration: Azure OpenAI, LangChain, LangGraph, Semantic Kernel, Microsoft AutoGen
• RAG & Vector DBs: Azure AI Search, FAISS, Pinecone, Weaviate
• Cloud & Data: Azure ML, Databricks, Azure AI Foundry, Azure Data Factory (plus equivalents on GCP/AWS)
• Infra & Ops: Docker, Kubernetes, CI/CD, Terraform (preferred)
• Agentic AI frameworks: ADK, Autogen, LangChain, LangGraph