Job Summary
The Machine Learning Engineer Lead will define and lead the architecture of scalable AI/ML and agentic systems across the global product portfolio. This senior technical leadership role will focus on large-scale distributed ML systems, LLM and RAG architectures, Agentic AI frameworks, tool orchestration, and enterprise platform engineering. The role will shape long-term AI platform strategy, establish technical standards, and mentor engineering teams while supporting highly available, secure, and scalable AI systems.
Key Responsibilities
• Define reference architectures for LLM, machine learning, and agent-based systems across products.
• Design high-availability and low-latency inference platforms capable of operating at global scale.
• Establish reusable platform components for model lifecycle management, deployment, monitoring, and operationalization.
• Architect scalable AI platforms supporting large-scale distributed ML systems and enterprise AI workloads.
• Architect multi-step, reasoning-driven agentic AI systems.
• Design orchestration patterns for tool use, API invocation, and structured function calling.
• Lead the implementation and governance of Model Context Protocol (MCP) servers to standardize tool integration and context management.
• Define guardrails, permissions, security controls, and audit mechanisms for enterprise-safe AI systems.
• Establish and maintain best practices for MLOps, CI/CD, observability, scalability, and system reliability.
• Design and implement scalable inference systems using containerization and Kubernetes.
• Drive the deployment and optimization of LLM, Generative AI, and RAG solutions in production environments.
• Design cloud-based AI/ML architectures across AWS, Azure, or GCP.
• Establish technical standards and architectural patterns for AI/ML and agentic systems across engineering teams.
• Embed Responsible AI principles into platform architecture and engineering practices.
• Provide technical leadership, mentorship, and guidance to senior engineers and engineering teams.
• Collaborate with cross-functional teams to influence technical direction and ensure alignment with enterprise AI platform strategy.
• Support people management, leadership, and team development activities as required.
Required Qualifications
• 10+ years of experience building and deploying production-grade machine learning systems at scale.
• Strong experience with LLMs, Generative AI, and RAG deployments in production environments.
• Strong Python development background.
• Expertise designing and implementing AI/ML systems in cloud environments such as AWS, Azure, or GCP.
• Hands-on experience with Kubernetes, containerization, and scalable inference systems.
• Experience designing agentic AI systems and tool orchestration frameworks.
• Experience implementing and governing MCP servers or structured architectures for tool integration and context management.
• Experience with large-scale distributed ML systems and enterprise platform engineering.
• Experience establishing MLOps, CI/CD, observability, and system reliability practices.
• Demonstrated people management, technical leadership, or mentorship experience.
• Strong understanding of high-availability and low-latency AI/ML architectures.
• Ability to define technical standards and influence architecture and engineering decisions across teams.
