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AI & LLM Engineering Lead - Talent Pool

GE Vernova

AI & LLM Engineering Lead - Talent Pool

GE Vernova

Remote

·

Remote

·

Full-time

·

1w ago

Benefits & Perks

Remote Work

Remote Work

Required Skills

AI/ML Development

Large Language Models

Python

Generative AI

MLOps

Cloud Infrastructure

Data Governance

Cybersecurity

Job Description Summary

The AI & LLM Engineering Lead will drive the strategy, design, and deployment of advanced Artificial Intelligence and Large Language Model (LLM) solutions to transform engineering, operational processes, and project execution. This role ensures secure, scalable, and compliant AI adoption within power automation workflows, enabling productivity gains, continuous learning, and long-term competitiveness in the utility sector.

Job Description

Key Responsibilities

  • Lead AI/LLM strategy, solution architecture, and implementation across Engineering, Operations, and Project Delivery.
  • Build and maintain LLM-based agents to support:intelligent processing of technical documentation,
  • automated design validation and engineering workflows,
  • testing and QA automation,
  • knowledge retrieval and contextual reasoning.
  • Integrate AI into core power automation workflows:IEC 61850 SCD engineering files, relay settings, SCADA HMI & logic, substation documentation, etc.
  • Establish AI governance, secure data pipelines, and compliance with utility-grade cybersecurity standards.
  • Partner with engineering managers and subject-matter experts to identify high-value AI automation opportunities.
  • Develop scalable pipelines for inference, fine-tuning, continuous learning, and lifecycle management in cloud and on-prem environments.
  • Evaluate and incorporate emerging AI technologies (RAG, vector stores, autonomous agents, internal copilots).
  • Monitor model performance, accuracy, drift, and cost; lead improvement cycles and risk mitigation.
  • Train and coach engineering teams on practical AI tools and adoption in daily workflows.
  • Ensure compliance with GE Vernova global standards, regulatory expectations, and utility-sector requirements.

Required Qualifications

Education

  • Bachelor’s or Master’s degree in Electrical Engineering, Computer Science, Software Engineering, or related technical field.

Professional Experience

  • Hands-on experience with AI/ML development and production deployment.
  • Proven track record in full product lifecycle: ideation, prototyping, development, operations.
  • Industrial, energy, or automation domain experience strongly preferred.

Technical Skills

  • Deep expertise with:Large Language Models, generative AI, and intelligent agents
  • Engineering workflow automation
  • Python and modern ML frameworks (e.g., Py Torch, Tensor Flow)
  • API-driven solution design and MLOps practices
  • Cloud infrastructure (AWS, Azure, GCP) and on-prem architectures
  • Data governance and cybersecurity best practices

Languages

  • Fluent English and Portuguese required
  • Spanish proficiency preferred

At GE Vernova, we are always searching for great talent. While we might not have a specific job for you today, we want to know about you when we do. As actual openings become available, you may be contacted to discuss a potential opportunity.”

Additional Information Relocation Assistance Provided: No

- This is a remote position

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About GE Vernova

GE Vernova

GE Vernova provides energy consulting, gas power, and grid solutions.

10,001+

Employees

Boston

Headquarters

Reviews

3.8

34 reviews

Work Life Balance

3.7

Compensation

3.7

Culture

3.8

Career

3.7

Management

3.6

77%

Recommend to a Friend

Pros

Good work-life balance and flexible environment

Opportunity for career growth

Competitive compensation and benefits

Cons

Room for improvement in processes

Internal communication could improve

Some organizational bureaucracy

Salary Ranges

309 data points

Junior/L3

Junior/L3 · Business Analyst

0 reports

$92,460

total / year

Base

-

Stock

-

Bonus

-

$78,591

$106,329

Interview Experience

4 interviews

Difficulty

3.3

/ 5

Duration

14-28 weeks

Experience

Positive 0%

Neutral 50%

Negative 50%

Interview Process

1

HR Interview

2

Digital Interview

3

Technical Rounds

4

Hiring Manager Interview