MLOps Engineer

What You’ll Be Doing

Support product teams in achieving their OKRs while championing best practices in data engineering and MLOps. Work closely with product teams to ensure they effectively adopt the tools, frameworks, and processes provided by the Data Platform team, enabling the building of scalable, efficient, and reliable data and ML solutions. Help teams implement robust data pipelines, model deployment workflows, monitoring strategies, and cost‑efficient practices to improve data‑driven capabilities.

Act as a critical bridge between product teams and the Data Platform team by gathering insights on real‑world challenges, gaps, and pain points in the existing platform. By surfacing these issues and collaborating with the platform team, contribute to the continuous improvement of internal tooling and infrastructure, ensuring it better serves engineers and data scientists. Blend hands‑on engineering with strategic impact, influencing product success and the evolution of the data platform.

Passionate about making a positive difference in society by improving the financial health of users. Aligns with company values and engineering principles that drive ways of working and software delivery.

• Data system design and scoping of work
• Solid experience with a data engineering language (Python ideal)
• Knowledge of at least one distributed processing framework; experience with streaming (e.g., PySpark, Flink)
• Containerisation and orchestration;
  Docker, Kubernetes
• Infrastructure as Code;
  Terraform
• Software engineering best practices – proficiency in Python (preferred), code quality and maintainability
• Good knowledge of different storage types and appropriate use (OLTP, OLAP, S3)
• Understanding of value and product thinking
• Experience working cross‑functionally; ability to work with data scientists, software engineers, and product managers to align ML initiatives with business goals

• Experience running a streaming platform and knowledge of stream–to–table and table–to–stream transformations
• Deep technical knowledge of core data structures, distributed processing; practical application over theoretical knowledge; aptitude for understanding concepts and reasoning over value
• Monitoring and alerting as it pertains to data systems
• Deploying APIs and systems outside the core data platform; willingness to learn
• Experience working with feature stores
• Experience building and managing ML pipelines (e.g., Kubeflow, MLflow, Airflow, Flyte)

• Competitive compensation package (base plus equity) with bi‑annual reviews aligned to quarterly OKR planning cycles
• Work at a fast‑growing tech startup backed by top VC firms
• Clear progression plan; opportunities to lead, challenge the status quo, and own impact
• Flexibility to maintain work‑life balance; globally distributed team with remote options
• Generous annual leave (25 days plus additional days per year at the company, up to 30 days) and public holidays
• Private medical insurance (Alan)
• One‑month paid sabbatical after four years at the company
• Generous pay increases for high‑performing team members; equity top‑ups for promotions
• Company‑wide performance reviews every six months
• OpenAI subscription covered
• Online mental health support via Spill
• Other benefits including paid time off for social activities, virtual socials, and annual off‑site with expenses paid
• Employment through EOR provider (Deel) with benefits package details available upon request

We strongly encourage applications from people of colour, the LGBTQ+ community, people with disabilities, neurodivergent people, parents, carers, and people from lower socio‑economic backgrounds. If there’s anything we can do to accommodate your specific situation, please let us know. Your personal data will be processed in accordance with Cleo AI’s Candidate Privacy Notice.