Responsible for departmental operations planning/execution or is focused on execution of professional activities within a technical discipline. Functions with some autonomy but guided by established policies or review of end results.
The job allows modification of procedures and practices covering work as long as the end results meet standards of acceptability (quality, volume, timeliness etc.).Job Description
Essential 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.
Bachelor’s or Master’s degree in Electrical Engineering, Computer Science, Software Engineering, or related technical field.
hands-on experience with AI/ML development and production deployment.
Deep expertise with:
Large Language Models, generative AI, and intelligent agents
Engineering workflow automation
Python and modern ML frameworks (e.g., PyTorch, TensorFlow)
API-driven solution design and MLOps practices
Cloud infrastructure (AWS, Azure, GCP) and on-prem architectures
Data governance and cybersecurity best practices
Fluent English required
Spanish proficiency preferred
Hands-on experience with RAG pipelines, vector databases (FAISS, Milvus, etc.), and knowledge-graph integrations.
Familiarity with electrical system standards and engineering tools (IEC 61850, SCADA, protection & control).
Experience with CI/CD for ML, model versioning, and observability.
Certifications in AI, cloud architecture, or cybersecurity.
Demonstrated leadership in digital transformation initiatives.
Relocation Assistance Provided: No
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