MLOps Engineer

Quick Facts

Model zdalny 100% · Stawka: 175–200 zł netto/h (B2B)

Description

Rola polega na prowadzeniu ewolucji architektury live recommender system, rozwoju praktyk MLOps oraz przenoszeniu modeli z fazy research do produkcji. Będziesz aktywnie tworzyć kod w Python dla infrastruktury Data + ML, wdrażać modele na AWS SageMaker i zapewniać jakość, niezawodność oraz odtwarzalność workflow ML. Dodatkowo pełnisz rolę lidera technicznego i mentora dla data scientists oraz innych inżynierów.

Responsibilities

• Architect & Evolve: Lead the architectural evolution of our live recommender system to enhance its capabilities and quality.

• Champion MLOps: Drive strategy and execution for MLOps practices, including building/optimizing GitLab CI/CD pipelines, establishing robust monitoring, and leveraging MLflow for reproducibility.

• Productionalize Models: Transition ML models from research to production on AWS SageMaker with strict performance and reliability standards.

• Mentor & Advise: Mentor engineers in software engineering best practices, system design, and model deployment strategies.

• Hands-On Development: Contribute to Python codebase; write clean, maintainable, well-tested code for Data + ML infrastructure and model deployment.

• Collaborate & Innovate: Translate business needs into technical solutions with product managers, data scientists, and business stakeholders; advocate for new technologies to improve 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
• Proven track record of deploying and maintaining production ML systems
• Ability to design, document, and communicate architecture of complex ML and data pipelines
• Ability to collaborate with stakeholders to refine business requirements into actionable technical tasks
• Proven experience mentoring and providing technical guidance to data scientists, data engineers, and MLOps engineers
• Expert-level programming skills in Python and 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 (TensorFlow or PyTorch)
• Hands-on experience with the MLOps lifecycle including MLflow (experiment tracking and model management) and GitLab CI/CD
• Significant experience designing and building scalable ML systems in a major cloud environment (AWS preferred)

Benefits

• Model zdalny 100%
• Stawka od 175 zł netto/h do 200 zł netto/h (B2B)