Machine Learning Engineer

Machine Learning Engineer

Role Overview

Machine Learning Engineers play a pivotal role in bringing AI solutions from concept to production. They champion best practices in industrial-grade ML development and ensure that machine learning solutions are designed with scalability, reliability, and maintainability in mind.

By building production-ready ML pipelines or embedding existing models into enterprise-grade ecosystems, ML Engineers help bridge the gap between experimental data science and operational IT environments. Their work spans the full lifecycle of AI services—from development and deployment to monitoring and continuous improvement.

They act as a key interface between AI & Analytics teams and IT Production, ensuring that deployed models are supported by robust data pipelines, automation, and performance monitoring aligned with both technical and business needs.

Key Responsibilities

As part of Machine Learning initiatives, ML Engineers will:

• Collaborate closely with Data Scientists to design and implement solutions that account for production constraints from the outset, including infrastructure selection, deployment architecture, and serving mechanisms (e.g. batch vs. real-time processing, API design, data ingestion patterns).
• Contribute to the automation and industrialization of ML pipelines to support seamless integration and deployment into production environments (e.g. containerization, test automation, CI/CD implementation).
• Assist Data Scientists in leveraging standard enterprise platforms and tools to build, deploy, and monitor AI services effectively.
• Work alongside IT Production teams to configure and optimize target environments for AI workloads.
• Ensure models are stable and performant in production, retrained when necessary (manually or automatically), and monitored from both operational and business perspectives.

Experience & Technical Skills

Required Experience

• Minimum 4 years of relevant experience in Machine Learning engineering or similar roles

Mandatory Technical Skills

• Containerization and virtualization technologies
• AI platforms and development environments
• CI/CD pipelines (e.g. GitLab CI)
• Versioning of code, data, and models
• Advanced proficiency in Python
• Dependency and package management tools
• Relational databases (PostgreSQL)

Preferred Technical Skills

• Experience with system integrations across heterogeneous environments (distributed systems, mainframe, etc.)
• Model optimization and compression techniques
• ELT / ETL frameworks
• Big data ecosystems (e.g. Spark)
• Stream and data flow processing tools
• Data visualization solutions

Business Knowledge

• Solid understanding of Agile methodologies

Soft Skills & Competencies

• Strong written and verbal communication skills
• Highly results-driven with a strong sense of ownership
• Detail-oriented and methodical
• Creative problem solver with an innovative mindset
• Actively invests in continuous learning and skill development
• Demonstrates efficiency and effectiveness in delivery
• Challenges conventional approaches and explores new ways of working
• Displays energy, accountability, and commitment to achieving impactful outcomes
• Open-minded and constructive when engaging with change, feedback, and diverse perspectives