MLOps Engineer

Soldo is the proactive spend management solution that frees progressive businesses to accomplish more. Over 25,000 organisations across 31 countries use Soldo to end slow, messy, and inefficient spending, bringing financial agility and control over every expense. Soldo frees finance with a uniquely proactive approach to managing decentralised spending. By combining a powerful spend management platform, a user-friendly app, and versatile payment methods, Soldo automates expense admin to eliminate inefficiency in managing business spending. By proactively managing decentralised spend, organisations empower employees to spend when and where it's needed, keeping productivity high while avoiding month-end surprises. Founded in 2015 by Italian digital innovator Carlo Gualandri, Soldo is headquartered in London, with offices in Dublin, Milan, and Rome. We’re looking for people with big ambitions, cool heads, sharp minds, and warm hearts. Come and join us as we grow together.

What's in it for you Competitive salary Private healthcare coverage for you and your family Lunch Vouchers Genuine career development opportunities (we love to see you succeed) - including your own annual €500 career development budget Access to training and development - including a mentoring programme, workshops and the opportunity to progress onto our leadership programme Flexible working options, including working from home or our Milan or Rome offices + 60 days’ work anywhere Statutory Leave entitlements plus extra days off on Christmas Eve, New Year's Eve and your Birthday Your own personal company Soldo card Employee Assistance Programme CAF Annual Fiscal & Financial Support

The role As we continue to scale our AI capabilities, we’re looking for an MLOps Engineer to help us build and operate reliable, production-grade AI systems powering intelligent financial experiences across our platform. This role sits at the intersection of Machine Learning, Product Engineering and Cloud Infrastructure. You’ll work closely with AI Scientists, Software Engineers, and Product Managers teams to operationalise ML and GenAI solutions at scale, ensuring performance, reliability, governance and efficiency across the entire AI lifecycle. This is not a research-oriented Data Scientist role. We’re looking for an engineer with strong experience building scalable ML infrastructure and deploying AI systems in complex production environments.

Responsibilities Design, build and maintain scalable ML and GenAI infrastructure Productionise AI models and services across training, deployment and inference workflows Build and optimise CI/CD pipelines for ML systems Improve reliability, observability and monitoring of AI workloads Enable reproducible experimentation, model versioning and automated deployment processes Partner with AI Scientists to accelerate the transition from experimentation to production Optimise infrastructure performance, scalability and operational costs Support governance, compliance and security best practices for AI systems Contribute to the evolution of our AI platform architecture and engineering standards Drive operational excellence across the ML lifecycle

We’re looking for someone who has Strong experience in MLOps, ML Platform Engineering or AI Infrastructure roles Proven experience deploying and operating ML/AI systems in production environments Strong Python engineering skills Strong knowledge and experience with PySpark and Databricks Solid experience with cloud platforms such as AWS, GCP or Azure Experience with containerisation and orchestration technologies (Docker, Kubernetes) Experience building CI/CD workflows for ML systems Familiarity with ML orchestration and experiment management tools such as MLflow, Kubeflow, Airflow or similar Experience with monitoring, logging and observability tools Understanding of distributed systems and scalable backend architectures Experience working in cross-functional SaaS product environments Strong communication and collaboration skills

It would be nice if you have Experience with LLMOps and GenAI infrastructure Experience deploying RAG pipelines and LLM-based applications Familiarity with vector databases and inference optimisation Experience in fintech or highly regulated environments Infrastructure as Code experience (Terraform or similar) Exposure to feature stores and model serving frameworks