Data Science & Advanced Analytics :

- Lead the application of Statistics, Machine Learning, forecasting, prediction and optimization techniques to complex CPG/ Retail business problems.

- Design analytical approaches for large and complex datasets.

- Develop, validate, and optimize machine learning and forecasting models.

- Identify appropriate modelling approaches based on business objectives and data characteristics.

- Establish robust model validation and performance measurement frameworks.

- Translate complex analytical findings into actionable business recommendations.

Agentic AI & Generative AI :

- Design and develop production-grade AI agents and multi-agent systems.

- Define agent architecture, orchestration, tool usage and workflow design.

- Build LLM-based analytical workflows that can reason over business data and interact with tools, APIs and business systems.

- Design RAG solutions and retrieval strategies appropriate to the use case.

- Develop approaches for agent memory, state management and workflow execution.

- Design evaluation frameworks to measure accuracy, reliability, groundedness and quality of AI/agent outputs.

- Identify and address hallucination, reliability, and failure-mode issues in AI applications.

- Stay current with emerging Agentic AI and GenAI technologies and assess their practical application.

Solution Architecture & Engineering :

- Design end-to-end AI solution architectures covering Data Science, LLMs, agents, APIs, databases and deployment.

- Develop backend services using Python, FastAPI or equivalent frameworks.

- Integrate databases, ML models, LLMs, APIs and external tools.

- Review code and technical implementations for quality, scalability, and maintainability.

- Establish engineering and development best practices for AI solutions.

Cloud & Production :

- Lead containerization and deployment of AI applications using Docker.

- Deploy and operate AI services on AWS, Azure or GCP.

- Contribute to CI/CD, monitoring, logging and production reliability.

- Work with Platform/DevOps teams on scalable AI application architecture.

- Diagnose production issues and drive improvements in system reliability and performance.

Technical Leadership :

- Own AI solutions from problem definition through production.

- Break complex problems into executable technical components.

- Mentor Data Scientists and junior team members.

- Review technical approaches and provide guidance on modelling and AI architecture.

- Collaborate with Data Engineering, Software Engineering and Platform/DevOps teams.

- Work directly with business stakeholders and clients when required.

- Contribute to technical standards, reusable components and AI best practices within the organization.

Skills & Qualifications :

- Bachelor's or Master's degree in Computer Science, Data Science, Statistics, Mathematics, Engineering or a related discipline.

- 5+ years of experience in Data Science, AI, Machine Learning, GenAI or related areas.

- Strong Statistics and Machine Learning fundamentals: forecasting, prediction, optimization, model validation, large-scale data analysis.

- Strong Python and SQL skills.

- Significant hands-on experience with Generative AI / LLMs (RAG, embeddings, prompt engineering, LLM evaluation).

- Demonstrated experience designing and building AI agents or agentic workflows - including agent orchestration, tool calling, agent memory/state, and agent evaluation.

- Experience limited to prompt engineering, basic LLM API integrations or simple chatbot development is not sufficient.

- Experience taking AI/ML solutions into production.

- Experience developing APIs or backend services (FastAPI or similar).

- Working knowledge of PostgreSQL or an equivalent relational database, and Git/version control.

- Hands-on experience with Docker and at least one cloud platform (AWS, Azure or GCP).

- Strong problem-solving and analytical ability, with the ability to independently own technical problems and drive them to completion.

Preferred Skills :

- LangGraph, LangChain or equivalent agent orchestration frameworks.

- Advanced RAG architectures and vector databases.

- Multi-agent systems design.

- Guardrails and reliability engineering for LLM/agent outputs.