[Hiring] Lead AI Engineer @Axya

Role Description

Our product is only as good as the AI behind it. As Lead AI Engineer, you will own the technical direction and quality of Axya's AI systems:

• How they reason over messy enterprise data.
• How we measure whether they are right.
• How we ship improvements safely to production.

You will report to the CTO and work closely with Product, Customer Success, and the engineers who build our platform and integrations. You will be hands-on in the code and raise the technical bar for the team through architecture, evaluation, code review, and mentorship. As the team grows, your scope can grow with it.

What You'll Own

• The architecture of Axya's AI layer:
  • Agent and LLM workflows.
  • Retrieval, tool use, and how they connect to customer ERP and procurement data.
• Evaluation and quality:
  • Build the eval frameworks, test sets, and monitoring that tell us whether the copilot is accurate, reliable, and improving.
  • Be the person who can say "this is ready" and prove it.
• Production delivery:
  • Take AI features from prototype to dependable, observable, cost-aware production systems.
• Model and tooling decisions:
  • Choose, benchmark, and evolve models, vendors, and frameworks, balancing quality, latency, cost, and customer data constraints.
• Customer-grade reliability and security:
  • Work with the CTO and security lead to meet enterprise expectations on data handling, access control, privacy, and auditability.
• Technical standards and mentorship:
  • Set engineering practices for AI development and coach other engineers in applied AI.
• Closing the loop with customers:
  • Turn real customer usage and failure cases into the next round of improvements.

Qualifications

• Experience shipping LLM-based or ML-powered systems to production, with real users and real consequences, not only prototypes.
• Strong software engineering fundamentals: fluency in Python and comfort building and operating backend services.
• Hands-on experience with the core building blocks of applied AI: prompting and tool use, retrieval, agent and workflow orchestration, and evaluation.
• Sound technical judgment: you can make architecture tradeoffs, explain them clearly, and own the outcome.
• Technical leadership: setting direction, reviewing others' work, mentoring or leading projects, even without formal reports.
• Clear communication with both engineers and non-technical stakeholders.

Requirements

• Experience with enterprise or B2B products where reliability, security, and customer trust are non-negotiable.
• Experience with messy structured data from ERP, supply chain, procurement, finance, or operations systems.
• Experience building evaluation and observability tooling for non-deterministic systems.
• Experience in an early-stage or high-growth environment, or evidence of building something from a blank page.
• Experience managing cost, latency, and model-selection tradeoffs at scale.

Nice to Have

• Fine-tuning or model adaptation experience.
• Experience in manufacturing, procurement, or supply-chain domains.
• Familiarity with enterprise security and compliance expectations (SOC 2, data residency, etc.).
• Fluency in French.

AI & Technology Expectations

You will use AI to build AI. We expect you to use coding assistants, automated evaluation, synthetic test generation, and agentic workflows to move faster and raise quality, while applying your own judgment on what to trust. We care about how you multiply your own output and the team's, not which specific tools you favor. We also expect you to keep a clear view of the risks: hallucination, data leakage, drift, and cost.

What Success Looks Like

• After 30 days:
  • You understand the current architecture, the main customer use cases, and where the AI fails today.
  • You have shipped at least one meaningful improvement or fix.
  • You have a written assessment of the biggest technical risks and opportunities.
• After 90 days:
  • A baseline evaluation framework is in place and used in release decisions.
  • You have led delivery of one major AI feature or re-architecture to production.
  • Quality, latency, and cost are measured and visible to the team.
• After 6 to 12 months:
  • Accuracy and reliability on core customer workflows have improved by a measurable margin.
  • An AI engineering roadmap is co-owned with Product and the CTO.
  • Repeatable practices (evals, release process, observability) are followed by other engineers.
  • If hiring begins, you have shaped the profile and bar for the next AI and ML hires.

Benefits

• Unlimited vacation policy: because we believe in balance.
• Comprehensive health insurance: your well-being matters.
• Flexible work schedule: results matter more than hours.
• 100% remote: get the job done wherever you prefer.
• Be part of a high-impact, collaborative team making real change in a traditional industry.