ROLE & RESPONSIBILITIES: Define and build agentic system architectures that leverage Amazon Bedrock AgentCore and agent frameworks to enable multi-step reasoning and automated workflows. Lead technical strategy for model selection, fine-tuning, and inference, advising on cost vs. performance tradeoffs. Design and implement containerized deployment standards using Docker and Kubernetes to ensure consistent, scalable, and fault-tolerant ML operations. Architect secure, low-latency networking for model-to-service and service-to-service communication across private and public networks. Perform systems-level performance engineering: select appropriate compute accelerators, run load and stress tests, and conduct capacity planning for production readiness. Establish and operate MLOps and GenAI Ops practices, including CI/CD pipelines, model versioning, and deployment automation. Implement observability, logging, monitoring, and incident response for production AI systems to ensure operational excellence. Own end-to-end system design for AI workloads: data pipelines, model training, inference, orchestration, and lifecycle management. Integrate foundation models into enterprise RAG and tool-use pipelines, enabling complex, real-world use cases. Provide technical leadership and mentorship to engineers and stakeholders on architecture, best practices, and operational standards. QUALIFICATIONS/EXPERIENCE: Appropriate academic qualification such as Computer Science, Engineering or Statistics Demonstrated track record delivering large-scale AI solutions for enterprise customers, including end-to-end ownership of architecture, operations, and stakeholder engagement Submit your CV to: