Role overview Own the administration and operations of enterprise AI platforms, ensuring secure, scalable, and cost-efficient usage of LLM services. Act as the central point for platform governance, usage monitoring, and enablement of AI teams.
Key responsibilities
- Administer and manage enterprise AI platforms including OpenAI, Anthropic Claude, or Google Gemini
- Manage API access, keys, usage policies, and rate limits across teams and applications
- Establish centralized governance for AI usage including audit trails, compliance controls, and policy enforcement
- Monitor platform usage, performance, and costs; implement cost controls, budget tracking, and optimization strategies
- Build and maintain dashboards for AI consumption, latency, and model performance analytics
- Enable multi-model orchestration and routing across different LLM providers for resiliency and optimization
- Define and enforce security controls including data privacy, prompt controls, and access management
- Support platform onboarding for engineering and business teams, including SDK/API integrations and environment setup
- Collaborate with AI/ML, DevOps, and security teams to operationalize AI workloads
- Troubleshoot platform, API, and model-related issues; ensure high availability and reliability
- Enable governance for agentic AI workflows, including identity, permissions, and operational guardrails
- Drive platform standardization, best practices, and reusable frameworks across teams
Required experience
- 5+ years in platform engineering, cloud operations, or AI platform management
- Hands-on experience with at least one: OpenAI (ChatGPT, Azure OpenAI), Anthropic Claude, or Google Gemini
- Experience managing API-based platforms, including authentication, quotas, and usage monitoring
- Strong understanding of LLM/GenAI architectures, token-based usage models, and AI application patterns
- Experience in platform governance, cost optimization, and operational monitoring
- Familiarity with cloud platforms (AWS, Azure, GCP) and AI services
- Experience working with monitoring/observability tools and dashboards
- Strong troubleshooting and operational support mindset
Good to have
- Experience with AI gateway/platforms or multi-provider orchestration (LLM routing, failover)
- Exposure to MLOps, CI/CD pipelines, and model lifecycle management
- Knowledge of agentic AI systems, RAG architectures, and vector databases
- Familiarity with security, compliance, and responsible AI frameworks
- Scripting or automation experience (Python, APIs, Terraform)