Machine Learning Engineer

Luxoft; Remote; GBP 50,000 - 75,000; Full time

Job summary

A leading consulting firm in the United Kingdom is seeking a skilled Machine Learning Engineer to develop data-driven solutions for Digital Oilfield systems. The role focuses on building machine learning models to enhance operational decision-making and forecasting in oil & gas environments. Candidates should have a strong foundation in machine learning, proficiency in Python, and familiarity with oilfield datasets. Experience with cloud platforms and agent-based systems is a plus.

Qualifications

• 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, or engineering inputs.

Responsibilities

• Develop and maintain machine learning models for oilfield data.
• Collaborate with domain experts to identify ML opportunities.
• Build models for time series prediction and classification.

Skills

Machine learning

Data modeling

Applied statistics

Proficiency in Python

Knowledge of ML libraries

Familiarity with oilfield datasets

Collaboration skills

Tools

scikit-learn

XGBoost

TensorFlow

PyTorch

Job description

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.