Lead AI Engineer 4C

Role overview

Platform and delivery responsibilities

• Own DevOps, MLOps, and LLMOps platforms; design CI/CD and GitOps delivery for applications, models, and agentic workflows.
• Enable model-serving infrastructure, GPU orchestration, LLM inference autoscaling, model registries, and vector databases.
• Manage Kubernetes, Helm, service mesh, and API gateway layers for highly available deployments.
• Establish IaC, policy-as-code, cloud governance, and FinOps practices.
• Build observability for services and AI pipelines; own incidents, on-call, disaster recovery, and business continuity.
• Embed DevSecOps and align infrastructure with responsible AI and data governance requirements.

Leadership and partnership

• Manage and mentor DevOps engineers; define standards, career paths, and hiring plans.
• Partner with data science, applied AI research and development, product, and client teams on infrastructure design and SLAs.
• Assess new tools and platforms and provide build-versus-buy recommendations.
• Champion automation-first practices and DevOps/MLOps maturity roadmaps.

Must-have experience and skills

• At least 8 years of DevOps, SRE, or infrastructure engineering experience, including 2 years in technical leadership or team management.
• Strong Python, Bash, and Ansible skills; hands-on CI/CD tool experience.
• Deep Docker, Kubernetes (EKS/AKS/GKE), and Helm experience.
• Terraform is mandatory; working knowledge of CloudFormation, Bicep, or Pulumi is also specified.
• Expertise in at least two of AWS, Azure, or GCP, with working exposure to the other two; Git-based version control and GitOps experience.
• The provided description begins a production ML/AI workload requirement, including deployment pipelines and model-serving frameworks, but ends before that qualification is complete.