Mid-Level Machine Learning Engineer

Summary

Join EcoVadis's AI Center of Excellence as a Mid-level Machine Learning Engineer! Leverage data to solve business problems, deploy and maintain ML models, and design ML pipelines. You will build ML infrastructure, partner with scientists and engineers, and utilize Python, MLflow, and Azure. Based in Barcelona or remote from Spain, this role requires a degree in a related technical discipline, strong Python programming skills, and 2+ years of experience in production-grade ML projects. EcoVadis offers flexible working hours, wellness allowances, professional development, and various other benefits.

Requirements

• Degree in Computer Science, Mathematics, Engineering, or a related technical discipline
• Strong programming skills in Python as well as related ML libraries
• Industry experience on production-grade end to end ML projects (2 years)
• Knowledge of machine learning lifecycle, principles, and MLOps tooling (e.g. MLFlow, Kubeflow)
• Experience with REST API for ML model serving
• Understanding of software engineering best practices across the development lifecycle, including agile methodologies, coding standards, code reviews, source management, build processes, testing, CI/CD tools and operations
• Familiarity with cloud technology, preferably Azure
• Experience with docker and container orchestration

Responsibilities

• Leverage data to solve business problems of various business units at EcoVadis
• Experiment, deploy and maintain ML models by ensuring scalability and speed
• Design ML pipelines by applying the best practices in MLOps
• Build ML engineering infrastructure and systems to orchestrate batch and real-time pipelines
• Partner with scientists and engineers to make ML models accessible to end-users and downstream processes
• Leverage Python, MLflow, Azure stack (e.g., Azure cloud, Azure ML, Azure Data Factory) and Databricks to deliver end-to-end solutions

Preferred Qualifications

• Contribution to the open-source libraries
• Knowledge of data versioning tools (e.g. DVC)

Benefits

• Support with all the necessary office and IT equipment
• Flexible working hours
• Wellness allowance for mental and physical wellbeing
• Access to professional mental health support
• Referral bonus policy
• Learning and development
• Sustainability events and community involvement
• Peer recognition program
• Employee-led resource groups
• Hybrid work organization (from the office or from home)
• Remote work from abroad policy
• Meals and Transportation Vouchers (Cobee card)
• Dental Benefits
• Life & Accident Insurance + Private Health Insurance
• Paid employee volunteer day
• Paid moving day (1/year)
• Time off: 1 Community Service Day + 1 Personal Day
• Summer Hours in July and August (36 hours per week)
• Hybrid Monthly Allowance for electricity and Internet