Lead architecture for GenAI/LLM solutions using patterns such as RAG, tool/function calling, and agentic workflows.
Design secure enterprise knowledge integration and define data access/segregation controls.
Establish Responsible AI, privacy, and security controls for GenAI.
Provide technical leadership across stakeholders; document architecture decisions, risks, and roadmaps; guide teams on performance and cost optimization.
Define AI/ML target architecture and reference patterns aligned to business strategy, enterprise standards, and regulatory requirements.
Translate business use cases into end-to-end solution designs across data, ML, application, integration, and infrastructure layers.
Establish technical guardrails (security, privacy, scalability, performance, observability) for AI/ML platforms and workloads.
Review and approve architecture/designs; document decisions, trade-offs, and standards; mentor teams on best practices.
Design and govern the AI/ML platform and MLOps lifecycle (data ingestion, training, validation, deployment, monitoring, retraining).
Define CI/CD standards for ML (model versioning, reproducible pipelines, automated testing, approvals, rollback).