Machine Learning Engineer

Our requirements

• 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.

Your responsibilities

• 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.

About the project

Recruitment stages

• 1.
  CV Evaluation
• 2.
  Phone screening
• 3.
  Interview
• 4.
  Employment decision

TechnipFMC