Role Summary :

Design, build and deploy AI solutions based on machine learning, generative AI and agentic AI: LLM-powered assistants, RAG pipelines, autonomous and multi-step agents, and workflow automation on top of the data platform built by the Architect and Data Engineers.

Key Responsibilities :

- Leverage existing data and build machine learning based forecasting and help in supply chain predictions.

- Identify opportunities in the organization on building Agents to drive process improvements and optimization.

- Build and deploy LLM assistants, RAG pipelines and multi-step agent workflows.

- Own prompt engineering, model and tool selection (open-source vs. API-based LLMs).

- Establish evaluation, guardrails and human-in-the-loop checkpoints where stakes warrant.

- Own production deployment: latency, cost and monitoring of AI features.

- Partner with the Data Scientist where a use case needs both predictive ML and generative components.

What We Expect From You :

- Ship AI features that are reliable and evaluated, not demo-only proofs of concept.

- Design agentic workflows with clear guardrails and fallbacks.

- Keep a close eye on inference cost and latency; a demo too slow or expensive for production is not done.

- Stay current on the GenAI/agentic tooling landscape and bring back what is actually useful.

Qualifications & Skills :

- 4 - 7 years in software or ML engineering with 1 - 2+ years on LLM/GenAI/AgenticAI systems in production.

- Hands-on with RAG, vector stores, agent frameworks, evaluation harnesses and API-based plus open-source models.

- Strong Python; cloud deployment experience (Azure OpenAI or similar) preferred.

- Sound judgement on cost, safety and reliability trade-offs.