ML Ops Engineer, AI Platform
Core
Build and maintain the production infrastructure for AI products (moderation, recommendation, fraud detection) to ensure reliability and speed.
Role type
Senior MLOps Engineer (AI Platform)
Builds
Online inference APIs, asynchronous workers, batch applications, and continuous learning pipelines for LLMs and ML models.
Domain
AI/ML infrastructure on AWS and Google Cloud
Deliverable
production ML models
Required skills
Kubernetes (EKS), GitOps (Argo CD, Helm, Kustomize), AWS/Google Cloud, Python, Go, SRE practices, observability, security/privacy, CI/CD, batch/asynchronous processing
Preferred skills
LLM model migration, Argo Workflows, SLI/SLO design, Terraform, dependency/security patch automation, supply chain security, data retention policies, AI agents for operations
Technologies
Amazon EKS, Argo CD, Argo Workflows, KEDA, External Secrets Operator, OPA Gatekeeper, Cluster Autoscaler, Helm, Kustomize, Amazon SageMaker, Amazon SQS, Amazon S3, Amazon Athena, AWS Glue, Amazon Aurora, Amazon DynamoDB, BigQuery, Gemini Enterprise Agent Platform, Datadog, Fluent Bit, Wiz, Conftest, GitHub Actions, Docker, uv, Ruff, mypy, pytest, Renovate, Dependabot, Jira, Confluence, Slack, Claude Code, Codex, ChatGPT, Cursor, Gemini Enterprise
Responsibilities
Manage Kubernetes cluster upgrades, node groups, and autoscaling; redesign observability and alerting for AI applications; define and operate SLI/SLOs with SRE team; design and operate mechanisms for updating Python dependencies, runtimes, and security patches; lead model/SDK transitions (e.g., Bedrock, Gemini) with quality regression checks; collaborate on security and privacy for AI infrastructure; operate continuous learning pipelines using Argo Workflows and SageMaker; manage online inference APIs and batch applications; reduce toil using coding/AI agents; build harnesses (docs, lint, tests) for AI agents.
Seniority
Senior, hands-on IC
