Staff Site Reliability Engineer - Dev Platform

Building the Future of Open Finance

Payward - the parent company behind Kraken, NinjaTrader, Breakout, xStocks, Payward Services and CF Benchmarks - has spent the last 15 years building one of the most modern and globally accessible financial infrastructure platforms in the industry, built to advance an open, global financial system.

The Team

Founded in 2011, Kraken is one of the world's longest-standing crypto platforms, trusted by over 10 million individuals and institutions across the globe. It offers spot trading, margin, futures, staking, and OTC services, with products built for both individual investors and institutional clients.

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.

The Opportunity

• 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

Skills You Should HODL

• 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

Nice to Haves

• 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

Our commitment

As an equal opportunity employer, we don't tolerate discrimination or harassment of any kind, whether based on race, ethnicity, age, gender identity, citizenship, religion, sexual orientation, disability, pregnancy, veteran status, or any other protected characteristic as outlined by federal, state, or local laws.

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