Artificial Intelligence Engineer

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. (ref:hirist.tech)