Lead AI/ML Engineer

Responsibilities • Lead end-to-end model development lifecycle (MDLC): design, architecture, development, testing, and deployment. • Architect and implement agentic AI systems leveraging AWS SageMaker, Bedrock, and related ML services. • Collaborate with data scientists to operationalize OpenAI GPT-4.1, Claude Sonnet, and other LLMs. • Design and integrate multi-agent microservices architectures in cloud environments. • Consume traditional ML models (e.g., regression, CatBoost) within agent-based workflows. • Provide technical guidance to other ML engineers and ensure best practices in scalability, monitoring, and compliance. • Stay current with emerging AI/ML frameworks such as AgentCore and Vision Core. • Communicate effectively with technical and business stakeholders in a fast-paced environment. Required Qualifications • 12+ years of hands-on ML engineering experience, including solution design and deployment (Must have delivered prior soltutions in-prod) • Strong proficiency in Python and key ML/AI libraries. • Deep experience with AWS AI/ML ecosystem: SageMaker, Bedrock, and related tools. • Proven ability to independently lead multiple concurrent projects. • Experience implementing agentic AI systems or multi-agent architectures in production. • Understanding of model consumption in API-based or microservice architectures. • Strong communication and collaboration skills. Preferred Qualifications • Familiarity with AgentCore, Vision Core, or other emerging agentic frameworks. • Exposure to OpenAI, Anthropic, or GPT-based model integration. • Experience in financial services or insurance domain (a plus, not required).