Machine Learning Engineer

AI/Machine Learning EngineerContract to Hire (3 Months)US Citizens Only Remote at first, but then most relocate to MilwaukeeRates Negotiable A large US enterprise with global operations has built out its machine learning and generative AI platforms on Azure. The ML platform is now mature enough for general use across the business — but it was built without the operational scaffolding underneath it. There is no CI/CD, no release automation, and no hands-off operating model, so the maintenance overhead sits with a team that has plenty of analytics depth but very little platform-ops depth.This role exists to fix that. You are being brought in to stabilize the ML platform, put proper engineering discipline around it, and then grow it into something the business can depend on. There is executive appetite and budget behind getting it right — the pain is real and it is visible.The role sits inside the Analytics & AI function of a wider enterprise data organization (data operations and governance, data engineering, and analytics and AI). The team is distributed internationally.What You’ll Own•    Stabilizing, maintaining and extending an enterprise Azure ML platform that is already in production use.•    Building the CI/CD layer that does not currently exist — Git repositories, branching and release controls, automated object deployment, infrastructure releases.•    Containerization and the parallel environment model for development, test and deployment.•    Automating the operational overhead out of the platform so it can run hands-off rather than consuming the team’s time.•    Supporting and growing the generative AI platform alongside the ML platform as adoption increases.•    Directing external delivery partners doing hands-on build work, and making sure their output matches the intended architecture.Must-Have Experience•    Real platform ownership. You have designed, deployed or taken ownership of a genuine enterprise ML platform — not a proof of value, not a notebook environment, and not a vendor-managed service someone else ran. This is the single biggest filter on the role.•    Azure, not "a hyperscaler". Azure Machine Learning and Azure AI Foundry, specifically. The client is explicit that they cannot absorb the ramp-up time of someone transferring comparable skills from AWS or GCP. You need to already know these tools.•    Deep automation capability. Demonstrable depth in automating infrastructure releases, object deployments and Git-based controls. Not "I have used a pipeline" — you have built the automation.•    Career profile. 5–8 years of technical experience across ML, AI and analytics platforms, including at least one full year hands-on in a platform operations capacity like this one.•    Vendor and delivery leadership. Every role on this team leads external resources. You need a track record of guiding contractors, consultancies or project teams to deliver against a defined vision.Nice to Have•    Hands-on experience of a true enterprise generative AI platform — rare, and treated as a strong differentiator rather than a requirement, since most organizations still buy this as a service.•    Copilot and Copilot Studio rollout experience at enterprise scale, including the adoption side rather than just the deployment.•    Exposure to consolidating a fragmented analytics tooling estate.How You’ll WorkThis is not a purely hands-on-keyboard role, and you should want that. The team operates at roughly four external resources to every internal engineer — sometimes higher. Expect around 20% of your time building, designing and developing yourself, and around 80% leading the outside teams doing the delivery work alongside you.You are not expected to be a project manager and the client is realistic about that. You are expected to understand how to steer external resources so their work lands in line with the architecture and the plan.The Person•    Strong prioritization instincts, and the resilience to handle genuine priority shifts without it derailing you. The client is upfront: occasionally you will be a mile deep in something and have to put it down. It happens less than it used to, but it still happens.•    Comfortable articulating your own contribution. In interview you will be asked exactly what you built and how — candidates who can only describe what "the team" delivered will not get through.•    Flexible, collaborative, and able to work across a distributed international team.Location & Working PatternThe permanent role is Milwaukee-based, four days a week on-site. For the contract phase there is flexibility for a candidate who is relocating to start remotely and increase on-site presence over the first few months — provided there is a clear, agreed path with milestones attached. An open-ended remote arrangement will not work for this role. Candidates already in the Milwaukee area, or genuinely committed to relocating, are the strongest fit.Compensation•    Contract phase: TBC•    On conversion: $140,000 base salary.•    5% short-term incentive bonus, tied entirely to company performance.•    401(k) matching and a broader benefits package.This is positioned mid-range against the lead-level engineering salaries within the function.Interview Process•    Stage 1 — Enterprise architect, and potentially a manager.•    Stage 2 — Second round with the same group plus other directors.•    Stage 3 — Final with the hiring director.•    A short technical exercise is possible but not expected.Next StepsThis is a confidential search. Client details are shared with shortlisted candidates only, on a one-to-one basis.