Own the reliability and performance of Kraken's AI Gateway, a critical service used across the company.
Posted by employer 1 day ago
First seen on Joblaze 1 hour ago
Last verified on the company career page 1 hour ago
Skills & Technologies
What you'll build
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AI in the day-to-day
AI Gateway provides self-service access to leading models from providers like Anthropic, OpenAI, and Google.
Not disclosed in this posting: compensation, years of experience, work arrangement, visa sponsorship.
Joblaze summary
In this role, the Staff Site Reliability Engineer will focus on ensuring the reliability and performance of Kraken's AI Gateway, a critical service for the company's operations. Key skills include extensive experience with Kubernetes, infrastructure as code, and a strong grasp of observability and incident management. This position is suited for senior professionals with a background in site reliability or platform engineering, particularly those who have managed high-traffic services. The role also involves mentoring engineers and collaborating with various stakeholders to enhance the platform's security and efficiency.
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From the original posting
The Dev Platform team builds the internal platform that Kraken's engineers use to build, ship and run software. Within Dev Platform, the AI Gateway is Kraken's company-wide LLM gateway: a single, governed entry point that gives teams self-service access to leading models from providers such as Anthropic, OpenAI and Google, with visibility into usage and cost, configurable budgets and rate limits, and routing across models and providers for efficiency and resilience.
Own the reliability, availability and performance of the AI Gateway as a production-critical service used across the company, including SLOs, error budgets and on-call practices
Design the gateway's resilience: multi-provider routing, failover, retries, rate limiting and graceful degradation when an upstream model provider is slow or unavailable
Set the technical direction for gateway architecture, capacity planning and scaling as LLM traffic grows and more workloads move onto the gateway
Build observability for LLM traffic, covering latency, errors, token usage and cost, attributed by team and use case
Partner with Security, Finance and AI governance stakeholders to put access controls, budgets, audit logging and data-handling requirements into the platform
Lead incident response and blameless post-incident reviews, and turn what you learn into lasting reliability improvements
Automate operations through infrastructure as code, safe deployments and progressive rollouts
Mentor engineers and raise the operating standard across Dev Platform
Extensive experience in site reliability, infrastructure or platform engineering, with a track record of owning high-traffic, business-critical services in production
Deep hands-on experience with Kubernetes, a major cloud provider and infrastructure as code (for example Terraform)
Strong command of SLOs, observability and incident management, and the judgment to know which reliability work matters most
Proficiency in a language such as Rust or Python, and comfort reading and contributing to a production service's codebase
Experience with API gateways, reverse proxies or traffic management: load balancing, rate limiting, failover and caching
Experience with Consul and Vault for service discovery, configuration and secrets management
Working knowledge of authentication and authorization mechanisms for internal platforms and APIs, such as OAuth 2.0/OIDC, JWT, mTLS and API keys, and how to apply them to a multi-tenant service
Experience with model evaluations: building or operating eval pipelines to compare models, prompts and agents, and to catch quality regressions before and after rollout
Familiarity with coding agents and agent harnesses (for example OpenHands, Claude Code or similar), including running them safely in sandboxed environments for engineering teams
Experience with agent memory systems and the databases behind them (vector stores, search, relational and key-value stores), and operating them reliably
Staff-level influence: you set direction across teams, write clear design documents and bring others with you
Good judgment balancing reliability, cost and developer velocity for internal customers
Experience operating LLM gateways or integrating LLM provider APIs at scale (streaming, token-based rate limiting, model routing, caching)
Experience hosting and serving models yourself, including vLLM, and running distributed inference or training workloads with Ray (GPU scheduling, batching, autoscaling, cost per token)
Experience designing, developing and maintaining Kubernetes controllers and operators (for example with controller-runtime or kubebuilder), including CRDs, reconciliation loops and safe upgrades
Experience with cost attribution, budgeting or FinOps for usage-based services
Background in security-sensitive or regulated environments such as financial services or crypto
Contributions to open-source gateway, proxy or observability projects
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