Lead AI Platform Engineer

What you'll do

• Lead the design, development, and operation of enterprise platforms for machine learning and generative AI workloads.
• Translate business and engineering requirements into scalable platform solutions and implementation approaches.
• Design and maintain AI systems that meet enterprise standards for security, reliability, and performance.
• Build and optimize services for model training, evaluation, fine-tuning, deployment, and inference.
• Implement repeatable MLOps capabilities and automated workflows for the AI development lifecycle.
• Design observability capabilities including telemetry, performance monitoring, logging, and drift detection.
• Lead troubleshooting, root cause analysis, and incident response for complex platform issues.
• Drive engineering best practices across Infrastructure as Code, CI/CD, and automated testing.

What we're looking for

• Experience designing, building, and operating cloud-native platforms or large-scale distributed systems in enterprise environments.
• Experience delivering solutions across public and private cloud environments.
• Experience deploying and managing containerized workloads using Kubernetes or OpenShift.
• Advanced software engineering and automation experience using Python.
• Experience leading the technical design and delivery of complex initiatives involving multiple teams or stakeholders.
• Experience implementing CI/CD pipelines, Infrastructure as Code, automated testing, and modern DevOps practices.
• Experience designing or supporting production AI, machine learning, data platforms, or distributed systems.
• Strong understanding of MLOps, model lifecycle management, production inference, and AI system operations.
• Experience implementing telemetry, monitoring, logging, alerting, and operational analytics.
• Ability to diagnose complex issues, lead root cause analysis, and implement sustainable corrective actions.
• Ability to assess architectural options, explain technical tradeoffs, and influence cross-functional decisions.
• Ability to communicate effectively with technical and non-technical stakeholders.
• Experience guiding, mentoring, or reviewing the work of other engineers.
• Experience with enterprise generative AI platforms and large language model workloads (preferred).
• Experience with model evaluation, fine-tuning, guardrails, responsible AI controls, and model governance (preferred).
• Experience with model observability, drift detection, telemetry, and operational analytics (preferred).
• Experience with AI serving infrastructure, inference platforms, or AI gateways (preferred).
• Experience supporting GPU-based workloads and accelerated computing environments (preferred).
• Experience with OpenShift, Kubernetes, Docker, Helm, GitOps, and Infrastructure as Code (preferred).
• Experience with enterprise platform engineering, developer enablement, and self-service capabilities (preferred).
• Familiarity with vector databases, retrieval-augmented generation, AI gateways, agentic systems, and model routing (preferred).
• Experience operating in regulated environments requiring strong security, governance, risk management, resiliency, and compliance controls (preferred).