Our Tech Stack

AI: Python

Frontend: Vue.js and Next.js

Backend: C# - ASP.NET

Infrastructure: Azure, GitHub, Linear

What you’ll do (responsibilities)

• Design and Deliver an agentic layer and a rich context graph that delivers automation of work with deep integration in our customers knowledge repositories.
• Drive architecture & delivery of production LLM/GenAI systems (context engineering, agentic workflows, model evaluations, multi-model strategies).
• Define and implement evaluation: offline + online metrics, gold sets, regression tests, human review loops, and A/B experiments.
• Operationalize LLMOps: deployment patterns, observability, monitoring, incident response, and performance/cost optimization (latency, throughput, token spend).
• Build guardrails & security: prompt-injection defenses, data-exfiltration prevention, permissions for tools/actions, and safe handling of sensitive data.
• Establish engineering standards: reference implementations, reusable libraries, review practices, and documentation that accelerates teams.
• Mentor and influence: coach engineers, raise the bar on system design and code quality, and align multiple teams on outcomes and timelines.

What we’re looking for (minimum qualifications)

• Extensive experience in AI/ML software engineering (or equivalent), with multiple production AI/ML launches.
• Strong software engineering fundamentals (system design, testing, reliability, code review, APIs).
• Proven experience with LLM application patterns (e.g., context engineering, output quality, embeddings/vector search, prompt design, structured outputs).
• Hands-on experience building evaluation + monitoring for model/system quality in production.
• Comfort operating in cloud environments (Azure), containers, CI/CD, and modern observability.
• Excellent cross-functional communication: you can translate ambiguity into plans, tradeoffs, and shipped outcomes.

Nice to have

• Experience optimizing inference (caching, batching, routing, quantization) or serving open-source models.
• Experience building internal AI platforms (evaluation harnesses, prompt/version management).

What success looks like in 90–180 days

• Delivery of AI native capabilities in our platform, aligned with product goals and risk constraints.
• A repeatable evaluation + release process that prevents regressions and supports fast iteration.
• AI capabilities shipped with monitoring, guardrails, and measurable business impact.