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Machine Learning & Artificial Intelligence Engineer

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Onsite

Machine Learning & Artificial Intelligence Engineer

PS0052 GE Energy Services France SARL

Machine Learning & Artificial Intelligence Engineer

Skills

Data ScienceJavaMachine LearningAILinuxNo-code AutomationsWorkday

2 months ago

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Important information


Contract type:

Permanent

Location:

Rugby CV23, UK

Work mode:

Onsite

Published on:

13 August 2026

What they need


Job Description Summary

Become part of a winning team and help to deliver the Green Energy transition.

This position will be responsible for developing ML & AI methodologies.

Main focus of the role:
o APOW (Automated Process and Optimisation Workbench) - tool reconstruction, develop this tool and develop new ideas.

o Programming

o Machine Learning & AI

o Mentoring and supervising a team of 4

GE Vernova is a multi-national organisation, employing over 80,000 people worldwide.

In the GE Vernova Gas Power Analytics & Digital engineering team, based in Rugby, we are constantly evaluating methods and algorithms that offer the opportunity to improve the efficiency and effectiveness of our engineering design and analysis tools and processes. Our main area of interest is automation and optimisation of engineering design processes.

We enjoy a multi-cultural working life at GE Vernova, and promote a friendly and productive working environment.

Job Description

Roles and Responsibilities

  • We require for a computer science engineer. This role will include the study of ML-AI techniques for our engineering applications. The scope of work is expected to be, but not limited to
  • AI Engineer and Machine Learning focus
  • APOW (Automated Process and Optimisation Workbench) - tool reconstruction, develop this tool and develop new ideas.
  • Mentoring and supervising a team of 4
  • Design, develop, test, and deploy clean, modular, and well-documented pipelines to build machine learning components, integrating them into the in-house platform to support the adoption of AI tools by engineers.
  • Maintain and enhance existing machine learning capabilities, including data management, computer vision and regression tools.
  • Translate stakeholder challenges and requirements into actionable, effective solutions.
  • Stay up to date with the latest advancements in AI techniques and tools to ensure the application of state-of-the-art solutions.

Qualifications

  • Post-graduate degree in Computer Science, Statistics, Machine Learning or Data Science.

Experienced required

Advanced and demonstrated experience in:

  • Solid foundation of Digital and Web methodology implementation.
  • Understanding of MLOps principles and practices.
  • Significant Java programming skills, including Swing and Java-FX.
  • Experience in Linux & Windows operating systems.
  • Good communication skills.
  • Good, adaptable team worker, eager to learn and develop.
  • Ability and confidence to work independently.
  • This role involves work for customers who manage critical infrastructure; based on customer requirements it will be necessary to carry out background checks and suitability assessments (e.g. UK Security Clearance (SC)) as part of the hiring process.

Hands on experience with (or similar tools) APOW:

APOW (Automated Process and Optimization Workbench) is an in-proprietary software tool. It allows design and engineering teams to build automated computational workflows, minimise human error, and perform advanced multi-variable optimisation for complex engineering systems.

Core Functions and Capabilities

  • Workflow Automation: Standardise routine calculation and simulation tasks (such as Computational Fluid Dynamics or CFD) to capture institutional knowledge.

  • Mathematical Integration: Connects established workflows with math routines to evaluate system performance.

  • Design Exploration: Runs Design of Experiments (DoE) to test how adjustments in shape or parameters affect overall output.

  • Optimisation & Machine Learning: Integrates optimisation algorithms and surrogate modeling (like Kriging models) to accelerate design enhancements and uncertainty quantification (UQ).

Typical Engineering Applications:

  • Used heavily in turbomachinery and power generation design.

  • Optimises physical components like turbine endwalls, casings, and stators to improve aerodynamic efficiency and reduce emissions.

About Us

GE Vernova’s Power Conversion & Storage business provides electrification systems that are critical to customers’ power and energy needs for their high performance applications. We work with some of the world’s major energy, maritime and industrial organizations, helping to enable a transition to energy efficiency and decarbonization, including through our specialist motors, power electronics systems, electrical drives and control technologies that evolve today’s industrial processes for a cleaner, more productive future. We serve specialised sectors such as energy, marine, industry and all related services.

Research and development are key to what we do, and you will have the opportunity to influence and play a critical role in the successful execution of programs for future surface ships, submarines and future technology programs along with the growth of a unique world class facility, its optimised operations and the development of new capabilities, tools and processes.

Additional Information

Relocation Assistance Provided: No

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