Senior Software Engineer ; MLOps

Position: Senior Software Engineer 1 (MLOps)
We’re Aioi R&D Lab - an AI tech hub in one of the fastest-growing insurance companies. We research and develop AI systems that catapult insurance from a slow-moving, traditional past into a data-driven, technology-lead and society-defining future.

We’re looking for dynamic, driven professionals like you to help evolve our business in new directions. You’ll need first-class credentials and a proactive attitude to help us drive the change the underpins our mission.

As a Senior Software Engineer 1 (ML Ops) you’ll be contributing to the design, build, and operation of cloud infrastructure supporting a privacy-preserving AI research programme, working under the direction of the Technical Lead. You will help deliver model hosting and training infrastructure, secure data storage, and agentic development tooling - the foundations the programme’s research tracks depend on.

If you’d like to be part of our brighter future, and share in our success, we’d love to hear from you.

Responsibilities

* Contribute to the design, build, and operation of cloud infrastructure supporting a privacy-preserving AI research programme, working under the direction of the Technical Lead.

* Help deliver model hosting and training infrastructure, secure data storage, and agentic development tooling – the foundations the programme’s research tracks depend on.

* Write clean, well-tested, maintainable code and contribute to shared engineering standards, CI/CD pipelines, and documentation.

* Support integration of infrastructure components with partner environments and research workflows, troubleshooting issues as they arise.

* Engage actively with technical trade-offs, ask good questions, and learn quickly as tool choices and requirements evolve throughout the programme.

* Working within a multi-partner programme where requirements evolve and final tool choices are not fixed from day one.

* Building robust infrastructure that research teams can depend on.

* Contributing effectively across a broad stack (compute, storage, serving, tooling) rather than specialising in a single area.

* Navigating the balance between moving quickly to support research timelines and maintaining engineering rigor and security standards.

* Growing technical skills and confidence in ML infrastructure through hands-on work, in an environment that values learning.

* Balancing innovation in generative AI with requirements around privacy, data sovereignty, security and operational trust.

* Ensuring that project outputs contribute not only to immediate delivery but also to longer-term reusable capability within the Lab.

Knowledge, Experience and Qualifications

Essential

* Extensive commercial software experience, with a track record of delivering working, maintainable code in a team setting.

* Solid Python skills, including core data and ML-adjacent libraries (pandas, numpy, scikit-learn) and good instincts around code structure, testing and packaging.

* Experience with cloud infrastructure at a practical level: deploying services, managing storage, working with access controls.

* Familiarity with Kubernetes and Helm.

* AWS experience preferred, strong experience with another provider considered.

* Experience with ML infrastructure or data engineering: training pipelines, model serving, experiment tracking, or data pipelines.

* Comfortable with CI/CD pipelines, version control, and containerisation as everyday tools, not just concepts.

* Able to engage with technically complex and ambiguous problems, ask good clarifying questions, research and develop new skills, and work iteratively toward solutions.

* Good communication skills: can explain what they’ve built, justify their decisions, explain what trade-offs they made, and flag up what they’re unsure about.

Desirable

* Exposure to LLM serving or fine-tuning workflows, even at small scale or in personal projects.

* Understanding of data governance or security requirements in regulated industries.

* Interest in privacy-preserving techniques (differential privacy, secure computation).

* Experience with agentic AI frameworks (Lang Graph, Auto Gen, CrewAI, or similar).

* Familiarity with Kubeflow, KServe, or similar ML orchestration…