Senior Applied AI Engineer

Axiomatic AI builds AI systems for scientific and engineering workflows. The Senior Applied AI Engineer bridges AI research and production software by developing LLM-powered product features, establishing production practices, and helping integrate, evaluate, and improve AI capabilities.

Responsibilities:

• Partner with users and internal stakeholders to identify high-impact workflows where AI can help
• Design and implement LLM-powered product features (agents, tools, prompting strategies) using frameworks like PydanticAI (or similar)
• Make principled trade-offs between models and approaches (quality, latency, cost, privacy, reliability)
• Enable AI developers to deploy their work reproducibly and safely
• Establish engineering standards for applied AI development: testing, reviews, maintainability, and operational readiness
• Collaborate with backend engineers to integrate AI capabilities into the product
• Mentor junior AI developers and supervise PRs to ensure high quality and consistent patterns
• Monitor, evaluate and optimize AI and agentic workflows
• Collaborate with specialist and scientist in bringing their expertise into the AI product
• Own applied AI features end-to-end: discovery → design → implementation → rollout → iteration
• Translate user feedback and behaviour into clear technical requirements and pragmatic delivery plans
• Build LLM workflows such as tool-calling agents, structured output pipelines, retrieval/tool integrations, and safe prompting strategies
• Iterate quickly while keeping production quality (readability, maintainability, debuggability)
• Select and evaluate LLMs (OpenAI/Anthropic/others) based on real constraints: quality, cost, latency, context limits, and reliability
• Develop prompt patterns and guardrails (structured prompts, schemas, constraints, fallbacks)
• Design and run lightweight evaluations to prevent regressions (golden datasets, acceptance criteria, failure-mode testing)
• Document model decisions and trade-offs in a way that enables other engineers to execute confidently
• Write production-grade code: clear abstractions, solid API boundaries, strong typing where appropriate, and consistent error handling
• Define and enforce testing practices for applied AI (unit tests, integration tests, golden/regression tests)
• Implement instrumentation appropriate for debugging and iteration (basic logging/tracing/metrics for AI features)
• Ensure reliability and security basics: rate limiting where needed, safe input handling, prompt-injection awareness, and sensible defaults
• Work with AI Developers to productionize their experiments regarding improving user workflows
• Define workflows: notebook/test repository → PR → staging → production
• Document AI infrastructure and best practices
• Review code and mentor AI developers on software practices