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.
- Robust Python; cloud deployment experience (Azure OpenAI or similar) preferred.
- Sound judgement on cost, safety and reliability trade-offs.

📌 Artificial Intelligence Engineer (India)
🏢 ChayanikaRecruittech
📍 India