-
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.
-
Independently design AI solutions tailored to client problems, translating business needs into scalable architectures.
-
Lead technical decisions in client engagements and actively contribute to pre-sales architecture discussions.
-
Mentor team members and define GenAI and ML best practices.