Key responsibilities

• Establish and maintain AI and agentic architecture blueprints, including RAG, orchestration, fine-tuning, and prompt pipelines
• Standardize AI deployment practices using containerized and serverless inference patterns
• Lead adoption of model lifecycle management across Dev, Stage, and Prod environments
• Partner with FinOps and Cloud Security to optimize cost, compliance, and control across AI workloads
• Own the MLOps reference stack, including MLflow, Kubeflow, Ray, Vertex AI, or custom platforms
• Define CI/CD processes for AI models including versioning, artifact tracking, and retraining workflows
• Build reusable SDKs, APIs, and templates for AI pipeline integration with Cloud Engineering systems
• Drive model observability and monitoring standards for drift, latency, and data integrity
• Lead the design and enablement of agentic AI systems, including LLM-driven orchestrators and multi-agent frameworks
• Create reference implementations and governance frameworks for RAG, memory, and action-based AI workflows
• Collaborate with product and data teams to move prototypes into secure, production-grade environments
• Embed AI security, data protection, and PDPL/GDPR compliance into the MLOps lifecycle
• Define model validation and explainability standards ensuring auditability and traceability
• Work with Cloud Security and Data teams on responsible AI controls and AIOps monitoring
• Mentor AI and ML engineers on scalable design patterns and operational excellence
• Contribute to internal AI guilds, tech councils, and engineering playbooks
• Represent Cloud Engineering in AI ecosystem evaluations and cross-functional initiatives