ML Ops Engineer do Serwisu (obszar Sales & Marketing)
RITS Group to firma informatyczna, która oferuje najwyższej jakości usługi programistyczne zarówno dla rynku polskiego, jak i międzynarodowego. Jesteśmy dumni, że należymy do grona najszybciej rozwijających się spółek technologicznych. Naszym priorytetem jest jakość oraz innowacyjność dostarczanych rozwiązań, a nasza kultura organizacyjna opiera się na zaufaniu, współpracy i ciągłym doskonaleniu.
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).