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AI Engineer (US)

Role overview

Qualifications

  • Production experience with LLMs
  • Strong software fundamentals: Python and/or TypeScript
  • Comfort with ambiguity and pace
  • Curiosity about the physical world

Responsibilities

  • Design and build agentic workflows using Claude, LangChain/LangGraph
  • Own evaluation and build eval harnesses and golden datasets
  • Take features from prototype to production
  • Join customer ride-alongs and site visits to inform code development

Key facts

Hard skills

Other skills

  • Curiosity
  • Dealing With Ambiguity
  • Problem Solving

About the company

Nexus Black logo

Nexus Black

Computer Software / SaaS

IFS Nexus Black is an industrial AI initiative created by IFS that solves hard industrial problems by putting AI to work for the people who keep the world turning, combining decades of industry expertise with world-class AI talent.

Company details

Company typeStartup
IndustryComputer Software / SaaS
Company size11 - 50

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Job description

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.

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.

The role

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.

What you'll do

  • Design and build agentic workflows — multi-step tool use, retrieval, and orchestration — using Claude, LangChain/LangGraph 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 (LangSmith), 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.

What you'll bring

  • 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 TypeScript, 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.

Nice to have

  • 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.

Why Nexus Black

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.

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MR

Marcus Rivera

Chief Revenue Officer

m.rivera@company.com
linkedin.com/in/marcusrivera
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