Lead ML Engineer
New
T
Team UpArtificial Intelligence
PolskaFull-TimeLead
Salary not disclosed
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Job Details
- Languages
- Pl B2, en B2
- Experience
- At least eight years of professional experience
- Required Skills
- DockerPythonSQLKubernetesMachine LearningNumpyPyTorchAzureCI/CDMLOps
Requirements
- At least eight years of professional experience in Machine Learning, Data Science, or a related field.
- Advanced Python and SQL skills, supported by commercial experience in developing ML solutions.
- Strong knowledge of tools and libraries such as pandas, NumPy, scikit-learn, XGBoost, or PyTorch.
- Experience with cloud-based environments, preferably Microsoft Azure.
- Practical knowledge of MLOps and infrastructure tools such as MLflow, Airflow, Docker, and Kubernetes.
- Experience designing and maintaining scalable data, model-training, and deployment pipelines.
- Familiarity with Git, CI/CD, automated testing, and software engineering best practices.
- Proven ability to deliver ML solutions from initial concept to monitored production deployment.
- A proactive, independent, and well-organised approach.
- Strong communication skills and the ability to cooperate with technical and non-technical stakeholders.
- English proficiency at B2 level or higher.
- Polish proficiency at B2 level or higher.
Responsibilities
- Develop AI and ML products throughout their full lifecycle—from experimentation and prototyping to production deployment.
- Design end-to-end Machine Learning systems focused on scalability, reliability, performance, and maintainability.
- Build and automate data, training, testing, and deployment pipelines using MLOps and DevOps practices.
- Implement model monitoring, versioning, retraining, and performance-control mechanisms.
- Solve complex prediction, optimisation, recommendation, and decision-support problems.
- Provide technical direction and coordinate work across Machine Learning, Data Engineering, and DevOps.
- Define priorities, promote engineering standards, and support effective project delivery.
- Translate business challenges into practical, production-ready ML solutions.
- Improve infrastructure efficiency and optimise the cost of cloud-based workloads.
- Collaborate with Product Owners and business stakeholders from problem definition through implementation.
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