Kraken - Senior Software Engineer

• Build and maintain Python services and tooling that support AI/ML use cases (e.g., APIs, integrations, automation, internal developer tools) and run reliably in production. • Help engineers adopt new models/tools from an engineering perspective - sharing best practices, patterns, and practical guidance. • Develop and evolve backend services (Django preferred) including business logic, ORM/data access patterns, admin tooling, and workflows. • Operate in AWS: deploy, run, and support AI-enabled systems; make sensible architecture/cost tradeoffs; partner effectively with infra/DevOps stakeholders. • Prototype and productionise LLM-powered features and integrations, using common LLM frameworks and MLOps tooling (see Tech Stack below). • Improve observability and reliability using Datadog (metrics/logs/traces, dashboards/alerts) and help establish good monitoring practices as we scale. • Communicate clearly across audiences - able to “talk tech to non-tech and vice versa,” produce strong documentation, and collaborate cross-functionally. • Engineers across Kraken can use new models/tools effectively, with clear engineering patterns, documentation, and reusable components. • You’re actively involved in shipping and supporting AI/ML integration tooling and improving day-to-day engineering workflows around AI. • Strong collaboration across AI Foundations, AI Foundry, and other engineering teams helps accelerate adoption; not hiring this role would slow AI adoption and impact team velocity. • Strong Python: senior/advanced capability designing components end-to-end, writing clean idiomatic code, testing thoroughly, and debugging complex production issues. • Solid software engineering fundamentals (system design, concurrency, code quality, testing strategy, maintainable architecture; strong reasoning about tradeoffs). • Cloud experience (AWS) running production services; comfortable owning reliability/scalability considerations and collaborating with platform/infra partners. • Strong communication and collaboration across technical and non-technical stakeholders. • Learning agility and drive: proven ability to ramp quickly on new domains/tools and deliver in evolving AI environments. • Django experience (preferred) and strong backend engineering patterns (security, performance, maintainability). • Familiarity with LLM frameworks / AI engineering tooling, such as Pydantic AI, LiteLLM, LangChain, and data/ML platforms like Databricks and MLflow. • Experience with Datadog for observability/monitoring in production environments. • Exposure to AWS Bedrock specifically (mentioned as part of the environment).