About the company

Our client is a global leader in the energy sector, specializing in both subsea and surface technologies. Their mission is to enhance the performance of the world’s energy industry. They achieve this by constantly challenging conventions and investing in over 20,000 employees across 48 countries. The company strives to provide an inspiring work environment, tackling some of the world's most complex technical and engineering challenges in collaboration with a truly global team

Work at Exadel - Who We Are 

We don’t just follow trends—we help define them. For 25+ years, Exadel has transformed global enterprises. Now, we’re leading the charge in AI-driven solutions that scale with impact. And it’s our people who make it happen—driven, collaborative, and always learning.

Requirements

• At least 5 years of experience in machine learning engineering, designing, building, and deploying advanced ML solutions.
• Proven ability to develop ML systems addressing complex problems, preferably within the oil and gas industry.
• Strong skills in Python (preferred), Java, SQL, or Scala, and experience with ML libraries such as scikit-learn, NumPy, Pandas, and joblib.
• Hands-on experience with TensorFlow, PyTorch, ONNX, and modern MLOps tools like MLflow, Docker, Kubernetes, and Airflow.
• Experience deploying ML models via APIs (FastAPI, Flask, TensorFlow Serving, or TorchServe) and managing end-to-end ML lifecycles.
• Proficiency in SQL/NoSQL databases (MySQL, PostgreSQL, MongoDB, Cassandra).
• Familiarity with cloud ML platforms (AWS SageMaker, Azure ML, GCP Vertex AI) and monitoring tools (Prometheus, Grafana, Seldon Core).
• Ability to manage project delivery, collaborate with cross-functional teams, and mentor junior engineers.
• Strong academic background in Computer Science, Statistics, Mathematics, or a related quantitative field.
• Awareness of Generative AI, foundation models, and LLMs, with motivation to stay current with ML trends.

English level
Fluent

Responsibilities:

• Optimize ML models through hyperparameter tuning, feature engineering, and algorithm selection.
• Collaborate with data scientists to translate prototypes into production-ready solutions.
• Design and implement scalable ML pipelines for training, validation, and deployment.
• Develop APIs and services to integrate ML models into enterprise applications.
• Ensure robustness and reliability through testing, CI/CD, and best software engineering practices.
• Monitor model performance in production and implement retraining or updates as needed.
• Deploy solutions on cloud platforms (AWS, Azure, GCP) using containerization tools (Docker, Kubernetes).
• Manage infrastructure for data ingestion, model training, and inference at scale.
  Apply model governance practices, including auditability, reproducibility, and compliance.
• Collaborate with cross-functional teams and communicate results to technical and non-technical stakeholders.
• Stay updated on ML engineering tools, frameworks, and deployment strategies, including deep learning, MLOps, and distributed computing technologies