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We are building a self-serve Enterprise AI Gateway that standardizes how every team uses large language models with strong governance. As a Lead AI OPS Engineer, you will design automation-first onboarding, controls, and observability for access, cost, and logging across the platform. Join a small, fast-moving team and apply now.
Responsibilities
- Design self-service onboarding for teams and agents, from initial request through to functional access
- Automate key provisioning, rotation, and permission updates
- Tune rate limits and quotas as adoption and usage increase
- Deploy and configure models including Vertex AI endpoints, vendor fallbacks, and Model Armor
- Operate MCP servers behind the gateway under the same governance rules as models
- Develop Python extensions that integrate the gateway with other enterprise systems
- Maintain the full platform as code using Terraform, Helm, and Jenkins
- Monitor and protect production health across availability, latency, and cost with alerting
- Assist teams using the platform and convert recurring questions into automation
Requirements
- Proven 5+ years of experience using Python for automation, extensions, and integrations
- Solid 5+ years of SRE experience with a track record of keeping production systems reliable
- Deep expertise in Kubernetes, preferably on GKE
- Hands-on experience with Google Vertex AI, especially endpoints for model serving and Model Armor
- Practical proficiency with Terraform, Helm, and CI/CD using Jenkins
- Working knowledge of GenAI/Agentic AI concepts (patterns, frameworks, protocols)
- Production exposure to an AI gateway (LiteLLM, EPAM DIAL or similar) is highly appreciated
- Very strong communication skills with the ability to explain platform behavior clearly to teams who use it daily
- English proficiency at B2 level (Upper-Intermediate) or higher
Nice to have
- Familiarity with GCP beyond GKE and Vertex AI, such as BigQuery, Cloud Run, and IAM
- Experience building AI agents, for example using Google's Agent Development Kit (ADK)
- Experience with AWS Bedrock