Machine Learning Engineer | Sustainability Tech
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Senior ML Engineer | Sustainability Tech Unicorn
- Location: Barcelona (hybrid, 4 office days per month) or fully remote from Spain
- Benefits: flexible working hours, 36-hour working weeks in July and August, private health insurance
About the company
Our client is a global leader in business sustainability ratings. Its assessments cover more than 130,000 companies across 175 countries, and large international corporations use them to evaluate the environmental, social and ethical performance of their suppliers.
The role sits within its AI Center of Excellence, a team of more than 20 scientists and engineers. The team works in small cross-functional groups that are embedded in product squads and own the AI component of each product.
About the role
The Senior ML Engineer takes the models developed by the data science team into production and makes sure they run reliably, efficiently and at scale.
- This is an engineering position
- It does not include model development.
Responsibilities
- Model productionisation: turn data science models into optimised, production-grade services, and work with scientists through the handover process.
- ML pipelines: design and maintain end-to-end pipelines that apply MLOps and LLMOps best practices.
- Orchestration: build and operate the infrastructure that supports both batch and real-time workloads.
- Model serving: make models available to applications and downstream processes through REST APIs.
- Monitoring: set up performance monitoring, drift detection and alerting for models in production.
- Infrastructure: manage infrastructure as code, CI/CD pipelines and containerised deployments.
- Engineering standards: help define and maintain the team’s engineering practices.
Requirements
- At least 6 years of experience in ML engineering, including experience from before the generative AI era
- A background in software or backend engineering
- Proven experience deploying, serving and monitoring ML models in production
- Advanced proficiency in Python
- Hands-on experience with Docker, Kubernetes, CI/CD, infrastructure as code and observability tools
- Experience with MLflow and with relational and non-relational databases
- Cloud experience, preferably with Azure (Azure ML, Azure Foundry, Bicep) and Databricks
- Experience working with data scientists to deliver models to production
- Eligibility to work in Spain (visa sponsorship is not available)
Who should apply?
Experienced ML engineers with a software engineering background who have built and maintained ML systems in production, ideally in a product-led, fast-paced environment. The role suits candidates who want to focus on the engineering and infrastructure that bring models into production rather than on developing the models themselves.
- Nice to have: production experience with Azure or Databricks, and contributions to open-source projects.
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