Senior Data Scientist

Lead Data Scientist (Generative AI / LLM)

Role Summary

We are looking for a Lead Data Scientist with strong hands-on expertise in Generative AI and Large Language Models (LLMs) to design and build scalable AI-driven solutions. The ideal candidate will combine deep technical proficiency with leadership capability, actively contributing to coding, model development, and solution architecture.

This role requires someone who can develop production-grade AI applications, lead technical initiatives, and collaborate closely with cross-functional teams to translate complex business problems into impactful AI solutions.

Key Responsibilities

AI Solution Development

• Design and develop AI/ML solutions leveraging Large Language Models (LLMs) and modern Generative AI frameworks.
• Build and deploy LLM-powered applications
  
  , such as conversational AI systems, intelligent assistants, and enterprise knowledge tools.
• Architect Retrieval Augmented Generation (RAG) frameworks integrating enterprise data with LLMs.

Hands-on Engineering

• Write high-quality, production-ready code using Python and modern AI/ML libraries.
• Develop and optimize prompt engineering strategies, LLM workflows, and agent-based systems.
• Build applications using frameworks such as LangChain, LlamaIndex, or similar orchestration tools.
• Implement vector search pipelines and semantic retrieval systems.

AI Architecture & Deployment

• Design end-to-end AI architectures integrating data pipelines, vector databases, and LLM services.

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• Build scalable pipelines for model deployment, monitoring, and lifecycle management.
• Integrate AI models with enterprise platforms, APIs, and data ecosystems.

Technical Leadership

• Lead and mentor a team of data scientists and AI engineers.
• Establish best practices for model development, experimentation, and code quality.
• Provide technical guidance on architecture decisions and AI solution design.

Stakeholder Collaboration

• Partner with product teams, engineering teams, and business stakeholders to identify and prioritize AI opportunities.
• Translate business requirements into scalable AI solutions and technical roadmaps.
• Communicate technical concepts and results effectively to leadership and cross-functional teams.