AI Engineer

About Nexus Black

Nexus Black is an AI-first business within IFS, built to move quickly, challenge traditional ways of working, and turn the potential of AI into real-world outcomes for enterprise customers. We build AI to solve hard problems for the industries that keep the world turning: up on power lines, down on factory floors, and out in the field.

Nexus Black is an AI-first business within IFS, built to move quickly, challenge traditional ways of working, and turn the potential of AI into real-world outcomes for enterprise customers. We build AI to solve hard problems for the industries that keep the world turning: up on power lines, down on factory floors, and out in the field.

Our products — Resolve (field service and predictive maintenance), Flow (production planning), Comply (regulatory compliance) and Reason (analytical reasoning) — are used by technicians and engineers who cannot afford a tool that doesn't work first time. We pair 40 years of IFS industrial depth with the technical pace of a start-up, and we get results in weeks, not months.

We're building a team of highly capable, high-ownership people who are comfortable operating across traditional boundaries. We care less about rigid job descriptions and more about people who can understand a problem, get hands-on, and own the outcome.

You’ll build the agentic systems behind our products: agents that reason and orchestrate, deterministic code that calculates, and every output traceable. You’ll ship production code from week one, and then watch a field technician use it on a customer site, because that’s where our engineering standard is set.

• Design and build agentic workflows — multi-step tool use, retrieval, and orchestration — using Claude, Lang Chain/Lang Graph and whatever earns its place next.
• Own evaluation. Define what "correct" means for a workflow, build eval harnesses and golden datasets, and hold the line: if it wouldn’t convince a 30-year veteran, it doesn’t ship.
• Take features from prototype to production: observability, tracing (Lang Smith), error handling, and reliability that survives week six, not just the demo.
• Join customer ride-alongs and site visits — plants, hangars, the field — and bring what you learn straight back into the code.
• Work with coding agents daily (Claude Code, Cursor) — they’re how we work, not a novelty — and keep making your own job smaller.
• Contribute like an owner: PR reviews, design docs, and a culture of writing things down.

• Production experience with LLMs — you’ve shipped systems using model APIs (Anthropic, OpenAI or similar), not just notebooks: system-level prompt design, agent architectures, tool use, RAG.
• An evaluation habit. You’ve built eval pipelines — golden datasets, regression suites, LLM-as-judge where it’s honest — and you can tell a good demo from a system that holds up in production.
• Strong software fundamentals:
  Python and/or Type Script, Git, testing, CI, and the judgement to know when deterministic code beats a model call.
• Comfort with ambiguity and pace — small team, broad remit, shipping over ceremony.
• Curiosity about the physical world. Our users fix aircraft and keep grids running; you should want to understand their job well enough to build for it.

• Retrieval infrastructure at scale (vector stores, hybrid search, reranking).
• Fine-tuning or model-adaptation experience where it genuinely beat prompting.
• Experience in industrial, field-service or other asset-heavy domains.
• Open-source contributions or a portfolio of shipped AI systems.

We’re a small team building AI that has to work the first time, because the people using it are forty feet up a utility pole. AI tooling isn’t a side experiment here — it’s how we work every day. If you want your code in the hands of people who keep the world running, we’d like to talk. Less talk. More impact.