Experience working with cross-functional teams and driving end-to-end solution delivery earning domain expertise between components
AI First mindset - mentor AI expertise to the team
Knowledgeable to drive sizing initiatives at the ideation phase and represent team in sharing technical solution with design artifacts. Specialization in design using AWS and AI/ML agentic LLM model expertise is key.
Architect and lead development of agentic AI systems capable of autonomous decision-making and task execution.
Design and implement secure ML execution environments using AWS services (e.g., SageMaker, Lambda, ECS, IAM, Bedrock, Agent Core, Agent Core gateway, Strands / Langgraph ).
Collaborate with product and engineering teams to influence product grooming by proactively identifying and shaping technically feasible requirements
Be fluent in design artifacts to support agile development and drive cross-functional alignment.
Absorb and evolve existing thin-router-based architectures to incorporate advanced AWS concepts, including emerging services like AWS Agent Core.
Provide technical guidance and mentorship to the team on MCP protocol usage, ensuring scalable and modular communication across agentic components.
Guide the team in leveraging AI tools programmatically using languages such as Python, Java, and others as needed.
Define and enforce security protocols for sandboxing, data privacy, and model governance.
Evaluate and implement frameworks for agent orchestration, memory, tool use, and multi-agent collaboration.
Mentor engineering teams and establish best practices for scalable, secure AI development.
Guide in hiring high performance team initiatives

Required Qualifications

Proven experience in agentic AI development, including autonomous agents, tool use, and memory systems.
Deep expertise in AWS cloud architecture, including ML and security services.
Strong background in machine learning, LLMs, and AI system design.
Experience with sandboxing, secure execution, and zero-trust architectures.
Demonstrated ability to influence product strategy through technical insights and feasibility analysis.
Influencing to provide right quality coverage using AI solutioning tools
Ability to absorb and extend existing architectural patterns, especially thin-router designs, using modern AWS services.
Hands-on experience with MCP / A2A protocol and ability to mentor teams on its application in distributed agentic systems.
Proficiency in AI tool integration using programming languages such as Python, Java, and others.
10+ years in software engineering or AI development, with 3+ years in tech leadership roles.
Delivering using agile / scrum models.
Own to drive devops dependencies

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