Machine Learning Engineer

As a dynamic multinational tech company operating in 50 countries, we drive innovation and create projects that shape the future and greatly enhance the quality of life. You will find our solutions in the space industry, supporting scientists in the development of cancer drugs, and implementing innovative technological solutions for industrial clients worldwide. These are just some of the areas in which we operate!

Currently, for the new Seargin project, we are looking for a Machine Learning Engineer to help evolve our successful, globally-deployed recommender system. This system is already delivering significant value to our internal business teams across many countries, and we are now ready to bring it to the next level of maturity. In this pivotal role, you will take ownership of enhancing the architecture and operational backbone of this critical system. This is a senior, hands-on position that blends deep technical expertise with mentorship. You will be responsible for advancing our ML systems, with a strong focus on strengthening our MLOps foundation using modern tools. You will also act as a key technical advisor to our data scientists, guiding them on best practices for building robust, scalable, and production-ready models.

Contract type: B2B

Location: remote

Seniority: senior

Cooperation:full-time

Responsibilities:

• Architect & Evolve: Lead the architectural evolution of our live recommender system to enhance its capabilities and quality.
• Champion MLOps: Drive the strategy and execution for our MLOps practices. This includes building and optimizing CI/CD pipelines in GitLab, establishing robust monitoring, and leveraging MLflow to ensure the reproducibility of our ML workflows.
• Productionalize Models: Collaborate with data scientists to transition advanced machine learning models from research to production on AWS SageMaker, ensuring they meet strict performance and reliability standards.
• Mentor & Advise: Act as a technical leader and mentor for data scientists and other engineers. Provide expert guidance on software engineering best practices, system design, and model deployment strategies.
• Hands-On Development: Be an active, hands-on contributor to our Python codebase. Write clean, maintainable, and well-tested code for Data + ML infrastructure and model deployment.
• Collaborate & Innovate: Work closely with product managers, data scientists, and business stakeholders to translate business needs into technical solutions. Proactively identify and advocate for new technologies to improve our ML capabilities.

Requirements:

• Bachelor's degree in Computer Science, Engineering, a related technical field, or equivalent practical experience.
• 5+ years of professional experience in a machine learning engineering role with a proven track record of deploying and maintaining production systems.
• Proven ability to design, document, and communicate the architecture of complex ML and data pipelines.
• Ability to collaborate with stakeholders to refine business requirements into actionable technical tasks.
• Proven experience in mentoring and providing technical guidance to data scientists, data engineers, and MLOps engineers.
• Expert-level programming skills in Python and a deep understanding of its data science ecosystem.
• Practical experience building, training, and deploying models using AWS SageMaker.
• Proven experience with at least one major deep-learning framework (e.g., TensorFlow, PyTorch).
• Hands-on experience with the MLOps lifecycle, including specific expertise in MLflow for experiment tracking and model management, and GitLab CI/CD for automation.
• Significant experience designing and building scalable ML systems in a major cloud environment (AWS preferred).

Nice to have:

• Master's or PhD in Computer Science, Artificial Intelligence, Machine Learning, or a related field.
• Familiarity with advanced model monitoring and observability tools (e.g., Prometheus, Grafana, Evidently AI).
• Excellent communication skills with the ability to articulate complex technical decisions to diverse audiences.