Lead ML Ops Engineer, Marshmallow

• You are a strategic engineering leader who blends long‑term thinking with hands‑on delivery
• You can articulate a compelling 3 to 5-year vision for ML Ops at a scaling fintech, then translate that into an actionable roadmap
• You thrive in fast‑growing environments, where ownership is key and ambiguity is the norm
• You continually scan the horizon for emerging ML and GenAI technologies and rapidly assess how they should reshape our tool‑chain and operating model
• You have a track record of defining and owning ML Ops strategy, then landing it successfully to unlock measurable commercial value
• You build high‑performing teams through clear direction, coaching and a strong engineering culture, and you hold a high bar for reliability, observability and developer experience
• End-to-end ownership of ML platforms servicing real-time, customer-facing products in regulated industries (finance, insurance, etc.)
• Demonstrable success in hiring, developing and retaining high‑performing engineering teams, with evidence of managing, mentoring or leading
• Proven ownership of an ML Ops or ML platform roadmap that delivered clear business impact
• Production experience with the AWS ecosystem, especially SageMaker for training and hosting
• Strong Python skills and the ability to work and integrate with Java backend teams
• Hands‑on delivery of CI/CD for ML (e.g. TeamCity, Jenkins, GitHub Actions)
• Infrastructure‑as‑code with Terraform (or similar) and containerisation with Docker
• Designing monitoring solutions that detect performance, drift and data‑quality issues in production
• Leading or mentoring engineers, and influencing cross‑functional stakeholders

Desirable

• Our central Data Science & AI team owns the end‑to‑end lifecycle of every model at Marshmallow, from experimentation to production. As an insurer‑fintech, serving hundreds of thousands of customers, reliability and speed are critical - our models quote prices, detect fraud and power next‑gen GenAI experiences in real time
• You will lead the ML Ops function within the team, line‑managing two ML Ops Engineers and reporting to the Director of Data Science & AI. Day‑to‑day you will partner with pricing and central data scientists, platform engineers and external vendors to ensure our models ship quickly, safely and at scale
• Crafting and owning a multi‑year ML Ops roadmap that unlocks <24 h model iteration cycles and 99.9 %+ production uptime
• Scanning the horizon for emerging ML and GenAI technologies, and translating insights into practical pilots that keep Marshmallow at the forefront of innovation
• Setting and iterating the company‑wide MLOps & LLMOps strategy, covering classical ML and emerging generative‑AI workloads
• Hiring, coaching and inspiring a high‑performing ML Ops Engineering team with a culture of ownership and excellence
• Mentoring and developing the ML Ops Engineers, expanding the team as we grow and setting technical standards while promoting best practice across Data Science and Engineering
• Acting as the go‑to person for ML Ops questions from senior stakeholders and championing robust ML deployment across the business
• Making build‑versus‑buy decisions, evaluating new vendors and tooling, and negotiating contracts where relevant
• Championing a “You build it, you run it” culture so every data scientist can safely own their models in production
• Overseeing multiple deployment pipelines on AWS SageMaker (real‑time and batch inference) and integrating them with TeamCity for automated CI/CD
• Managing the relationship with Tecton as our feature platform vendor, advising Data Science teams on its use for training and inference, and overseeing related infrastructure cost and operation

• Initial call with our Talent Acquisition Partner - 30 mins
• A past experience interview where you will discuss your journey so far and ways of working with Paul, our Director of Data Science & AI - 60 mins
• A technical interview with one of our Principal Data Scientists + Sr. ML Ops Engineers - 60 mins
• A culture interview with a bar raiser to see if your work style fits our processes and values (and vice versa!) - 60 mins