Oferta wygasa za:86 dni
Tryb pracyZdalna
Rodzaj umowyKontrakt B2B
Tryb rekrutacjiRekrutacja stacjonarna
About the project
We are looking for an experienced Lead MLOps Engineer to join a team responsible for the development and further evolution of a globally deployed machine learning recommender system.
The system is already delivering significant business value across multiple countries and is now entering the next stage of maturity. In this role, you will take technical ownership of the MLOps foundation, ML infrastructure, deployment processes, and architectural evolution of the platform.
This is a hands-on technical leadership position combining software engineering, machine learning infrastructure, cloud technologies, and MLOps. You will work closely with Data Scientists, Data Engineers, Product Managers, and business stakeholders, while also providing technical guidance and mentoring to the wider engineering and data teams.
Your responsibilities
- Lead the architectural evolution of a live, globally deployed recommender system.
- Define and drive the MLOps strategy, standards, and best practices across the ML lifecycle.
- Design, build, and optimize CI/CD pipelines using GitLab CI/CD.
- Build and improve ML workflows focused on automation, scalability, reliability, and reproducibility.
- Use MLflow for experiment tracking, model management, and reproducible ML workflows.
- Productionize machine learning models and deploy them to AWS SageMaker.
- Collaborate closely with Data Scientists to move models from research/prototyping into reliable production environments.
- Design and maintain scalable ML and data pipelines.
- Implement robust monitoring, observability, and operational processes for production ML systems.
- Act as a technical advisor and mentor for Data Scientists, Data Engineers, MLOps Engineers, and other technical team members.
- Establish and promote software engineering best practices, including clean code, testing, documentation, and maintainability.
- Work hands-on with the Python codebase, developing ML and data infrastructure as well as deployment solutions.
- Translate business and product requirements into scalable technical solutions.
- Evaluate and introduce new technologies that can improve the organization's ML capabilities.
Our requirements
- 5+ years of professional experience in Machine Learning Engineering, MLOps, or a closely related role.
- Strong track record of deploying, operating, and maintaining production machine learning systems.
- Expert-level Python skills and strong knowledge of the Python data science ecosystem.
- Hands-on commercial experience with AWS SageMaker — this is a key requirement for the role.
- Strong understanding of MLOps principles and the end-to-end ML lifecycle.
- Practical experience with MLflow, including experiment tracking and model management.
- Hands-on experience with GitLab CI/CD and building automated ML/CI/CD pipelines.
- Experience designing and building scalable ML systems and data/ML pipelines in a major cloud environment, preferably AWS.
- Proven ability to design, document, and communicate complex ML and data architecture.
- Experience with at least one major deep learning framework, such as PyTorch or TensorFlow.
- Experience taking ML models from development/research through to production deployment.
- Ability to collaborate with Data Scientists and other stakeholders and translate business requirements into actionable technical solutions.
- Proven experience providing technical leadership, mentoring, and guidance to Data Scientists, Data Engineers, MLOps Engineers, or Software Engineers.
- Strong understanding of software engineering best practices, including testing, version control, code quality, and maintainability.
- Bachelor's degree in Computer Science, Engineering, or a related technical field, or equivalent practical experience.
- Master's degree or PhD in Computer Science, Artificial Intelligence, Machine Learning, or a related field.
- Experience with ML monitoring and observability tools such as Prometheus, Grafana, or Evidently AI.
- Experience with production recommender systems.
- Experience working with globally distributed ML platforms or systems.
- Strong understanding of model performance, reliability, scalability, and production monitoring.
- Excellent communication skills and the ability to explain complex technical concepts and architectural decisions to both technical and non-technical stakeholders.
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