Machine Learning Engineer

TechnipFMC leads the transformation of the energy industry by transforming our clients’ project economics through fully integrated projects, products, and services. Making robust decisions efficiently and consistently by using data about our products, processes, and operations is a key competency for our business to achieve our true north.

In this context, the business is developing its Advanced Analytics capability with the aim of better leveraging our data to deliver new insights, value and smarter ways of working across our value stream. Machine Learning Engineering is a key discipline in this context that focuses on designing, building, and deploying scalable machine learning systems and infrastructure to enable data-driven decision-making and innovation.

This role is for a Machine Learning Engineer who will be a member of the Advanced Analytics team (within Software Services) that is responsible for developing the company’s data analytics strategy and roadmap.

Health, Safety & Environment:

• 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

  
  

Performance & Delivery:

• 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.

• Ready to work from Krakow office 4 days in a week.

REQUIRED KNOWLEDGE/FORMAL EDUCATION:

• 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

  
  

 REQUIRED EXPERIENCE:

• 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.

REQUIRED TECHNICAL SKILLS AND/OR PROBLEM-SOLVING SKILLS:

• 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.