Generative AI Data Scientist

About the Role

We are seeking an innovative and highly skilled Generative AI Data Scientist to join our AI System Design team. This individual will play a key role in bridging business needs with advanced AI-driven solutions. The ideal candidate will have experience in end-to-end AI experiment life cycle management, architecture design, and the ability to translate business KPIs into measurable AI outcomes. A strong background in data management (preferably on Databricks), along with proficiency in modern Generative AI frameworks, is essential.

This role demands both technical depth and excellent communication skills to engage with business stakeholders, design scalable AI systems, and ensure reliable model deployment using Azure and Databricks.

Key Responsibilities

• Lead end-to-end AI experimentation processes from conceptualization to deployment.
• Collaborate with business stakeholders to define success metrics, KPIs, and measurable outcomes for AI applications.
• Translate business requirements into detailed AI solution designs, including:
• Business Value definition
• AI solution design (Data Flow, AI component selection, Prompt Engineering, AP, I, and AI integration planning)
• Automated model evaluation and monitoring frameworks
• Design, build, and scale data pipelines and AI workflows on Azure and Databricks ecosystems.
• Apply Generative AI techniques for text, code, image, and multi-modal use cases using advanced prompt engineering.
• Utilize modern GenAI toolkits and libraries, including:
• PyTorch
• LangChain, LangGraph, Model Context Protocol, Google's Agent Development Kit (ADK), and similar agentic frameworks.
• Develop experiments for LLM fine-tuning, retrieval-augmented generation (RAG), and multi-agent AI workflows.
• Deliver production-grade Python code using advanced data science, ML, and analytics libraries (eg, Pandas, NumPy, Scikit-learn, PyTorch, Hugging Face Transformers, Pydantic).
• Document and communicate AI designs, decisions, and experiment outcomes to both technical and non-technical stakeholders.
• Lead the design and deployment of automated AI performance evaluation frameworks.

Required Qualifications

• Master's or Ph.D. in Computer Science, Data Science, AI/ML, Mathematics, or related field.
• 5+ years of experience in applied data science, with at least 2+ years focused on Generative AI/LLMs.
• Strong applied knowledge of Databricks data engineering and ML workflows.
• Proficiency in the Azure AI/ML ecosystem, including MLOps and data management best practices.
• Demonstrated expertise in:
• Python (advanced)
• AI/ML libraries (PyTorch, Hugging Face, Scikit-learn, TensorFlow, optional)
• Prompt engineering and fine-tuning LLMs for task-specific use cases
• Familiarity with LangChain, LangGraph, MCP, or other agentic frameworks for building AI applications.
• Excellent communication and stakeholder management skills.
• Strong ability to connect business concepts with AI-driven architectures and KPIs.