Job Description
What will you do?
Own the architecture and delivery of production-grade LLM systems and classical ML solutions.
Design, evaluate, and optimize RAG pipelines (retrieval strategy, chunking, indexing, monitoring).
Build scalable, production-grade LLM services and agentic workflows, alongside traditional ML systems where appropriate.
Define architecture trade-offs (LLM vs traditional ML, fine-tuning vs RAG, hosted vs self-managed models), with a strong focus on system-level optimization (latency, cost, scalability, reliability).
Architect and optimize distributed GenAI and ML workloads on Databricks (Spark, MLflow), leveraging deep understanding of the platform ecosystem.
Implement evaluation frameworks to measure quality, hallucination, and performance.
Productionize systems with proper CI/CD, monitoring, rollback, and versioning.