AI Engineer — Agentic Systems & Enterprise AI

Our client is a major regulated natural gas distribution utility serving millions of customers across a large service territory in the western United States. The organization is in the midst of an enterprise AI transformation — deploying agentic systems, integrating large language models into SAP and operational workflows, and expanding its AI Center of Excellence. The team is growing and actively onboarding both onshore and offshore AI engineering talent.

We are seeking skilled AI Engineers — at both onshore and offshore levels — to design, build, test, and maintain production AI systems within a complex regulated utility environment. Depending on level, you will work on LLM integrations, agentic pipelines, RAG architectures, AWS infrastructure, Copilot extensions, and SAP data integrations.

This is a hands-on engineering role. You will implement the architectures defined by the AI Architect, build prompt pipelines and agent workflows, develop integrations, and participate in testing, evaluation, and continuous improvement of AI systems across the enterprise.

08 Openings

AI Engineer I: 3–5 yrs; LLM + AWS hands-on, building toward full-stack AI delivery

AI Engineer II: 5–8 yrs; agent frameworks, Bedrock, SAP API integrations, Copilot

Senior AI Engineer: 8–12 yrs; end-to-end agentic systems, regulated-industry production experience

Staff / Lead Engineer: 12+ yrs; technical leadership, cross-team AI system ownership

Required:

• Extensive professional software or AI/ML engineering experience.
• Hands-on experience building with large language models in production — Anthropic Claude preferred.
• Proficiency in Python and/or TypeScript for API development, agent logic, and data pipelines.
• Experience with at least one agentic AI framework: LangChain, LangGraph, AutoGen, CrewAI, or equivalent.
• Familiarity with AWS services: Lambda, S3, API Gateway, and at least one of Bedrock, ECS, or Step Functions.
• Understanding of RAG architecture: chunking, embedding, vector search, and retrieval patterns.
• Strong written communication — able to document systems clearly for both technical and non-technical audiences.

Preferred:

• Experience integrating AI systems with SAP S/4HANA or SAP BTP via REST/OData APIs.
• Microsoft Copilot Studio and Power Platform development experience.
• Exposure to regulated industries — utilities, energy, healthcare, or financial services.
• AWS Certified Developer or AWS Certified Machine Learning Specialty certification.
• Familiarity with Model Context Protocol (MCP) for agent tool integration.
• Experience with vector databases: Pinecone, OpenSearch, or pgvector.
• Knowledge of CPUC, NERC CIP, or FERC data governance requirements.