- Architect the AI/ML platform service and API surface for customer and internal use.
- Manage the end-to-end model and prompt lifecycle using Databricks MLflow and AWS Bedrock.
- Design inference substrates for real-time and batch AI workloads, including capacity planning and concurrency control.
- Implement RAG architectures and agentic layers including tool/function calling and agent runtimes.
- Develop security guardrails, PII/PHI handling, and tenant data boundaries within the AI platform.
- Build evaluation and quality infrastructure, including regression gates and drift detection.
- Establish observability, SLI/SLO/SLA models, and audit trails for validated environments.
- Contribute to infrastructure-as-code (CloudFormation/CDK) and deployment automation.
- Lead design reviews and mentor engineers on distributed systems and AI engineering practices.
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