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Careers

Lead AI Engineer

Lead the AI engineering workstream across one or more client engagements — owning technical direction, a small team of AI engineers, and what ships to production.

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Practice

AI Engineering

Location

Remote

Employment type

Full Time

Experience

7–10 Years

Role Overview

You will lead the AI engineering workstream on one or more client engagements, owning the technical approach end to end and the team delivering it. This is a hands-on lead role, not a hands-off one: you will still design agent architectures and review code, but you will also set the technical direction, defend it to client stakeholders, and make the calls that a workstream's success depends on. You will manage a small team of AI engineers, split work across a project, and be the point of technical accountability for what ships to production. If you have led a technical workstream through ambiguity to a GenAI feature that real users rely on, and want to do that with more scope and less oversight, this role is built for you.

What You Will Do

  • Own the technical approach for an AI engineering workstream or project, from architecture through to production
  • Lead and mentor a small team of AI engineers, reviewing their prompt, agent and code design and setting the technical bar
  • Make architecture calls on agent design, model selection and evaluation strategy, and defend them to technical and non-technical stakeholders
  • Break down ambiguous client requirements into a workstream plan with clear milestones and owners
  • Act as the primary technical point of contact for client product owners on your workstream
  • Review evaluation harnesses, guardrail frameworks and production readiness across the team before they reach a client
  • Escalate and resolve delivery risks early, including cost, latency, reliability and safety risks
  • Contribute to proposals and solutioning for new or expanding client engagements
  • Partner with data engineering and data science leads on cross-functional workstreams

Technical Skills

  • Languages & Frameworks: Expert-level Python, REST APIs, and async patterns for concurrent model calls. Comfortable reviewing others' code, not only writing your own.
  • LLM Systems: Deep expertise across provider APIs such as OpenAI and Anthropic. Prompt architecture at scale, structured output, and tool calling, with the judgement to set team standards.
  • Agent Orchestration & Architecture: Designing end-to-end multi-agent or agentic workflow architecture for an ambiguous problem, framework-agnostic and defensible to stakeholders.
  • Retrieval & Data: RAG architecture leadership — chunking strategy, embedding models, vector database trade-offs, and retrieval quality at scale.
  • Evaluation & Safety: Building evaluation harnesses and guardrail frameworks as a team standard, not a one-off. Setting quality bars for generative output.
  • Production & Deployment: Cost and latency optimisation, observability, CI/CD and cloud deployment at scale. Owning reliability across a workstream, not a single feature.
  • Leadership & Delivery: Running a workstream — planning, task allocation, architecture and code review, and reporting status to technical and client audiences.

Experience & Qualifications

  • 7 to 10 years of software or AI engineering experience, including at least 2 years leading a workstream, project or small team
  • A track record of shipping multiple GenAI features to production, across more than one client or domain
  • Experience managing or mentoring other engineers, with visible growth in the people you've worked with
  • Bachelor's degree in Computer Science, Software Engineering or a related field

Good to Have

Not required, but a strong plus if your experience already includes any of the following.

  • Experience in a consulting or professional services environment, managing client relationships directly
  • Experience building evaluation harnesses or guardrail frameworks from scratch, as a team standard rather than a one-off
  • Experience contributing to technical proposals, scoping or pre-sales conversations
  • Formal people-management experience, including performance reviews or career development conversations

Why Xunova

Remote-first, built on trust

A genuinely remote-first team, built around trust and ownership rather than hours logged.

Direct access to leadership

Direct exposure to client leadership from day one, with no layers between you and the work that matters.

Ownership that shapes the work

A high-growth environment where your judgment shapes how we work, not just what we deliver.

Colleagues who hold a high bar

A small, deliberately high-caliber team that takes the craft seriously and holds a high bar for each other.

How we hire

A straightforward, three-conversation process, no take-home busywork disconnected from the job.

Remote · Full Time
  1. 01

    Introductory conversation

    A conversation about your experience, what you're looking for, and what we're building together.

  2. 02

    Technical discussion

    Grounded in real engineering problems, not abstract puzzles disconnected from the work.

  3. 03

    Team conversation

    Time with the team you'd actually be working with, in both directions.

Ready to apply for Lead AI Engineer?

Send your resume and a short note on relevant experience. We read every application.

View all open roles