Machine Learning Engineer

We are seeking a skilled and domain-expert Machine Learning Engineer to develop and deploy data-driven solutions in the context of Digital Oilfield (DOF) systems. This hands-on technical role focuses on building ML models that enhance forecasting, optimization, and operational decision-making across complex oilfield environments. Experience with agent-based or generative AI systems is a bonus.

Must Have

• Develop and maintain machine learning models tailored to oilfield data and engineering processes.
• Work closely with domain experts to understand workflows and identify ML opportunities across production, reservoir, and facility systems.
• Build, train, and deploy models for time series prediction, classification, anomaly detection, or clustering using structured and semi-structured data.
• Validate model accuracy and performance in real-world operational settings.
• Collaborate with software teams to integrate models into DOF platforms or dashboards.
• Strong background in machine learning, data modeling, and applied statistics.
• Proficiency in Python and ML libraries such as scikit-learn, XGBoost, TensorFlow, or PyTorch.
• Familiarity with oilfield datasets, including production data, sensor logs, simulation outputs, or engineering inputs.
• Understanding of the challenges and context of oil & gas workflows, even if not from direct experience.
• Ability to collaborate with geoscientists, production engineers, or field operations teams to co-design effective models.

Good to Have

• Experience working with or developing for Digital Oilfield systems (DOF platforms, custom solutions, or commercial tools).
• Exposure to cloud platforms such as Azure (preferred) or AWS.
• Familiarity with Agentic AI frameworks (LangChain, CrewAI, AutoGen), or LLMs as a support layer in technical environments.
• Knowledge of MLOps practices or tools (e.g., MLflow, Airflow, or model deployment pipelines).
• Azure Data Engineer or AI Engineer certifications are a plus, especially for roles involving cloud-based deployment.
• AWS experience is appreciated but not mandatory.

Project description

We are seeking a skilled and domain-expert Machine Learning Engineer to develop and deploy data-driven solutions in the context of Digital Oilfield (DOF) systems. The ideal candidate will have a strong foundation in machine learning, model development, and data analytics, with the ability to apply these skills to subsurface and production engineering workflows. This is a hands-on technical role focused on building ML models that enhance forecasting, optimization, and operational decision-making across complex oilfield environments. Experience with agent-based or generative AI systems is a bonus, but not a requirement.

Responsibilities

• Develop and maintain machine learning models tailored to oilfield data and engineering processes.
• Work closely with domain experts to understand workflows and identify ML opportunities across production, reservoir, and facility systems.
• Build, train, and deploy models for time series prediction, classification, anomaly detection, or clustering using structured and semi-structured data.
• Validate model accuracy and performance in real-world operational settings.
• Collaborate with software teams to integrate models into DOF platforms or dashboards.
• (Optional but valued) Explore the use of LLMs or agentic AI to support technical queries or enhance interaction with data systems.
• business trip to Kuwait

Skills

Must have

• Strong background in machine learning, data modeling, and applied statistics.
• Proficiency in Python and ML libraries such as scikit-learn, XGBoost, TensorFlow, or PyTorch.
• Familiarity with oilfield datasets, including production data, sensor logs, simulation outputs, or engineering inputs.
• Understanding of the challenges and context of oil & gas workflows, even if not from direct experience.
• Ability to collaborate with geoscientists, production engineers, or field operations teams to co-design effective models.

Nice to have

• Experience working with or developing for Digital Oilfield systems (DOF platforms, custom solutions, or commercial tools).
• Exposure to cloud platforms such as Azure (preferred) or AWS.
• Familiarity with Agentic AI frameworks (LangChain, CrewAI, AutoGen), or LLMs as a support layer in technical environments.
• Knowledge of MLOps practices or tools (e.g., MLflow, Airflow, or model deployment pipelines).
• Certifications:
• Azure Data Engineer or AI Engineer certifications are a plus, especially for roles involving cloud-based deployment.
• AWS experience is appreciated but not mandatory

Other

• Languages
• English: C1 Advanced
• Seniority
• Senior