AI Engineer

What will your day look like?

Strong computer science and engineering fundamentals

• Excellent foundation in data structures/algorithms, system design, distributed systems concepts, and debugging complex production issues
• A “production engineering” mindset: reliability, performance, failure modes, observability, and operational readiness.

 

Extensive full-stack experience

• Proven experience building and operating modern web applications and services end-to-end. 
• Strong capability across typical ECC stack areas such as: 

1. 1. Frontend: React (or equivalent modern framework)
   2. Backend: Node.js, go, and/or Python; API design; event-driven patterns
   3. DevOps: CI/CD, automated testing, cloud-native deployment, observability 
   4. Data: relational + NoSQL design and operational considerations

 

Prompt engineering, model evaluation, and agentic AI

• Extensive hands-on prompt engineering experience (prompt design, versioning, testing, guardrails, and measurable iteration)
• Experience with LLM evaluation: quality, accuracy, safety/bias considerations, latency/cost trade-offs, and human-in-the-loop assessment.
• Solid understanding of agentic AI architectures (orchestrators, tool calling, controlled action spaces, decision traces, safe escalation)
• Familiarity with MCP concepts and patterns for building scalable model↔tool ecosystems (tool contracts, context handling, permissioning, telemetry). 

 

Communication and collaboration

• Ability to work across product, engineering, governance, and operations teams; communicate trade-offs and influence decisions with clarity