AI/ML Implementation Engineer

GE Vernova

2d•Remote

About The Position

GE Vernova is accelerating the path to more reliable, affordable, and sustainable energy, while helping our customers power economies and deliver the electricity that is vital to health, safety, security, and improved quality of life. Are you excited at the opportunity to electrify and decarbonize the world? We are seeking a visionary, results-driven and technically exceptional AI Project Engineer to join our HQ Quality Engineering & AI/ML Implementation team — a team where collaboration and participative leadership are not just words, but the way we work every day. This is your opportunity to create real impact from day one. As a core member of our quality engineering team, you will be at the forefront of our Quality 5.0 vision — where data intelligence and AI-powered tools redefine how we manage, predict and prevent quality issues across GE Vernova's global business. This newly created role exists to help our organization use AI to make quality processes more proactive, data-driven, and scalable. Instead of relying on manual inspection or reactive problem-solving, the AI Project Engineer enables smarter quality management through automation, prediction, and real-time insights. You will act as the critical bridge between our Quality Engineering domain and our Digital execution team — pushing for innovation and bringing the Quality 5.0 vision to life. Working together with our Data Scientist, you will be a core enabler of broad AI adoption across HQ quality processes, engineering tools, and our evolving Digital Quality Suite — stepping directly into a live, strategic initiative: our Quality Roadmap with AI integration. Reporting directly to the Head of Quality Engineering & AI/ML Implementation, you will be part of a passionate, international team that is rewriting the rules of quality in the energy transition era.

Requirements

Bachelor’s degree in data science, computer science, statistics, mathematics, engineering, or a related technical field.
3+ years of relevant experience in AI/ML engineering, data science, or a related technical discipline.

Nice To Haves

Master’s degree in data science, computer science, statistics, mathematics, engineering, or a related technical field.
Advanced AI/ML engineering proficiency — hands-on expertise in development with Python, FastAPI, and prompt engineering techniques (Zero-shot, Few-shot, Chain-of-Thought prompting).
Deep understanding of AI quality and robustness — responsible AI principles, model validation, security, bias mitigation, and production-grade AI reliability.
Basic knowledge of quality management processes and methodologies (e.g., APQP, 8D, FMEA, or similar quality KPI frameworks).
Proven self-driven delivery mindset — track record of independently driving projects from concept to production in a fast-paced, cross-functional enterprise environment.
Structured problem-solving with visionary thinking — ability to break down complex challenges into actionable plans while simultaneously driving a forward-looking AI adoption roadmap.
Strong collaborative mindset — able to define requirements clearly and work effectively with a distributed Digital execution team.
Proactive, intellectually curious, and comfortable operating in a dynamic, evolving environment.
Strong storytelling ability — capable of turning complex analytical findings into compelling narratives for business stakeholders.
Background in the energy, automotive, or large-scale manufacturing industries.
Startup or fast-paced scale-up experience — comfortable with ambiguity, rapid iteration, and delivering high-impact results with lean resources.
Familiarity with enterprise platforms such as SAP, Salesforce, or similar ERP/CRM systems from an AI and data integration perspective.
Experience with AWS cloud services from an AI/ML perspective (e.g., SageMaker, S3, Lambda, Bedrock).
Understanding of Semantic Data Models and data modelling concepts across heterogeneous systems.
Exposure to MLOps principles — model versioning, experiment tracking, deployment basics.
Familiarity with version control systems (e.g., Git) and collaborative development practices.
Experience in international, multicultural team environments and working across time zones.

Responsibilities

Leading the analysis of current AI adoption and integration maturity across business lines and defining a structured homologation approach for scalable AI deployment in quality processes.
Identifying and delivering quick wins in the field of preventive quality — translating business challenges into concrete AI/ML solutions with measurable impact.
Designing, developing, and deploying production-grade AI and ML applications that automate quality workflows, enable predictive quality insights, and reduce quality-related incidents.
Leveraging advanced prompt engineering techniques (Zero-shot, Few-shot, Chain-of-Thought) and Large Language Models (LLMs) to build intelligent quality tools that augment human decision-making and automate quality workflows.
Driving the development and execution of the aligned Quality Suite 5.0 AI adoption roadmap, informed by real business line requirements and delivering a minimum of three new AI applications in production within the first year.
Acting as the strategic and technical bridge between the Quality Engineering domain and the Digital execution team — defining data requirements, solution architecture, and AI use case prioritization.
Collaborating closely with Data Engineers, Data Scientists, and AI/ML engineers to ensure AI solutions are robust, secure, scalable, and production ready.
Leading in a project environment — influencing without formal authority, aligning cross-functional teams, and delivering results with pace and precision.
Engaging with internal business line stakeholders across GE Vernova's global organization to understand their quality data needs, gather requirements, and iterate solutions based on real-world feedback.
Contributing to the evolution of the Digital Quality Suite, bringing innovative ideas and a forward-thinking mindset to continuously improve our quality tooling landscape.
Documenting solution designs, model performance, and data definitions clearly to ensure transparency and reproducibility across the team.
Staying current with the latest advancements in AI, ML, and responsible AI practices, proactively proposing new approaches that enhance our quality solutions.

Benefits

medical, dental, vision, and prescription drug coverage
access to Health Coach from GE Vernova, a 24/7 nurse-based resource
access to the Employee Assistance Program, providing 24/7 confidential assessment, counseling and referral services
GE Vernova Retirement Savings Plan, a tax-advantaged 401(k) savings opportunity with company matching contributions and company retirement contributions, as well as access to Fidelity resources and financial planning consultants
tuition assistance
adoption assistance
paid parental leave
disability benefits
life insurance
12 paid holidays
permissive time off