Kraken is a remote-first crypto exchange serving over 10 million individuals and institutions. Its Agent Systems team is a 0→1 engineering group building internal AI-powered agents that interact directly with company systems to automate workflows and accelerate execution. The role focuses on designing and shipping production agent systems that use large language models, owning services end to end from architecture through deployment and monitoring, and working at the intersection of AI, backend systems, and applied product engineering.
Overview
Compensation not specified
remote
full-time
4-7 years of experience (senior level)
Technologies
- Large language models (LLMs)AI/ML systemsInference pipelinesBackend APIsDistributed systemsData modelingPrompt engineeringEvaluation frameworksModel performance tuningCRMSupport toolsAnalytics platformsDeveloper platformsInternal toolingGuardrailsSandboxingSafety layersAgent-based systemsAutonomous workflows
Responsibilities
- Build and ship AI-powered agent systems that automate internal workflows and decision paths.
- Design and implement inference pipelines that reason over user interactions across internal systems.
- Prototype quickly to validate agent behaviors, then productionize what proves valuable.
- Architect backend services that integrate with multiple internal APIs and data sources.
- Define patterns for safe, observable, and controllable agent execution.
- Collaborate cross-functionally to identify high-leverage automation opportunities.
- Improve system speed, reliability, and signal quality as usage scales.
- Contribute to foundational design decisions for a 0→1 agent platform.
- Own services end to end, from architecture through deployment and monitoring.
- Work closely with cross-functional partners across product, infrastructure, and internal operations to deploy agent-driven capabilities.
Qualifications
- 5+ years of experience building and shipping production software systems.
- Strong backend engineering fundamentals (APIs, distributed systems, data modeling, reliability).
- Experience building fullstack systems, with comfort working across backend and occasional frontend surfaces.
- Hands-on experience working with AI/ML systems, LLMs, or inference pipelines in production.
- Ability to prototype rapidly while maintaining a clear path to production-grade architecture.
- Strong systems thinking — able to reason about orchestration, failure modes, and observability.
- Comfort operating in ambiguous, 0→1 environments with high ownership.
- Nice to have: Experience building or operating agent-based systems or autonomous workflows.
- Nice to have: Familiarity with prompt engineering, evaluation frameworks, or model performance tuning.
- Nice to have: Experience integrating with internal enterprise systems (CRM, support tools, analytics platforms, etc.).
- Nice to have: Background in building developer platforms or internal tooling.
- Nice to have: Experience designing guardrails, sandboxing, or safety layers for AI systems.