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.

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.

Senior Software Engineer – AI Foundations

Key skills