Machine Learning Engineer
- Kraków, małopolskie
- Stała
- Pełny etat
- Complete mandatory HSE courses and implement any recommended safety actions efficiently.
- Be a consistent role model in relation to safety practices with a commitment to the importance of safety
- Optimize model performance through hyperparameter tuning, feature engineering, and algorithm selection.
- Collaborate with data scientists to translate prototypes into production-ready solutions.
- Design and implement scalable machine learning pipelines for training, validation, and deployment.
- Develop APIs and services to integrate machine learning models into enterprise applications.
- Ensure robustness and reliability of ML systems through unit testing, integration testing, and CI/CD practices.
- Monitor model performance in production and implement retraining strategies as needed.
- Leverage cloud platforms (e.g., AWS, Azure, GCP) and containerization tools (e.g., Docker, Kubernetes) for scalable deployment.
- Apply best practices in software engineering, including version control, code reviews, and documentation.
- Manage infrastructure for data ingestion, model training, and inference at scale.
- Implement model governance practices, including auditability, reproducibility, and compliance.
- Collaborate with cross-functional teams including DevOps, software engineers, and product managers.
- Stay current with advancements in ML engineering tools, frameworks, and deployment strategies.
- Utilize a broad range of technologies including deep learning frameworks (e.g., TensorFlow, PyTorch), MLOps tools (e.g., MLflow, Kubeflow), and distributed computing (e.g., Spark, Ray).
- Communicate results effectively to both technical and non-technical stakeholders.
- Required: Bachelor's degree in Computer Science, Statistics or Mathematics.
- Desirable: Master's or a higher degree in Computer Science, Statistics or Mathematics. or a related discipline
- Minimum of 5 years of experience in machine learning engineering, building and deploying advanced solutions using state-of-the-art ML techniques.
- Designing and implementing machine learning systems to solve problems in the oil and gas industry.
- Collaborating with business and technical stakeholders to deliver scalable and tailored ML solutions.
- Manage delivery of machine learning project milestones, ensuring on-time & on-quality deployment.
- Ability to evaluate and guide technical work performed by junior machine learning engineer.
- Advanced - Programming in Python (preferred), Java, SQL, and Scala.
- Advanced - Use of ML libraries and tools such as scikit-learn, NumPy, Pandas, and joblib.
- Advanced - Designing, training, and deploying ML models for diverse data types including tabular, unstructured (e.g., text, images), and time-series data.
- Advanced - Working with high-performance ML frameworks such as TensorFlow, PyTorch, and ONNX.
- Advanced - Using version control systems like Git for collaborative development and code management.
- Advanced - Managing the ML lifecycle using tools like MLflow, Docker, Kubernetes, and Airflow.
- Advanced - Building and exposing ML models via APIs using tools like FastAPI, Flask, TensorFlow Serving, or TorchServe
- Proficient - Implementing MLOps practices for production-grade ML pipelines on cloud platforms (e.g., AWS SageMaker, Azure ML, or GCP Vertex AI).
- Proficient - Monitoring and observability of ML systems using tools like Prometheus, Grafana, and Seldon Core.
- Proficient - Working with SQL and NoSQL databases including MySQL, PostgreSQL, MongoDB, and Cassandra.
- Proficient - Familiarity with generative AI, foundation models, and LLMs to stay aligned with emerging trends in ML engineering.