AI Engineer

Job Description AI Systems Development: Architect, fine-tune, and deploy AI agents purpose-built for utility use cases, including predictive operations, customer engagement, and energy optimization. Backend Integration: Build APIs, microservices, and orchestration frameworks that seamlessly connect AI models with enterprise systems and grid-level data flows. Pipeline Ownership: Design and manage the full AI pipeline — ingestion, embeddings, retrieval, evaluation, and continuous deployment — ensuring reliability and scalability. AI Risk Mitigation: Address vulnerabilities unique to AI, such as model drift, bias exploitation, adversarial robustness, hallucination control – with sensitivity to regulated environments. Cross-Functional Collaboration: Partner with software engineers, data specialists, and security teams to integrate AI capabilities, embedding AI by design into every product release. Speed of Delivery: Operate with urgency, delivering breakthroughs in code and AI services on cycles measured in weeks, not quarters. We are a company committed to creating diverse and inclusive environments where people can bring their full, authentic selves to work every day. Qualified candidates will receive consideration for employment regardless of their race, color, ethnicity, religion, sex (including pregnancy), sexual orientation, gender identity and expression, marital status, national origin, ancestry, genetic factors, age, disability, protected veteran status, military or uniformed service member status, or any other status or characteristic protected by applicable laws, regulations, and ordinances. Skills and Requirements 5+ years of applied ML/AI engineering experience, ideally with exposure to enterprise/mission-critical systems.

Track record of deploying AI services in production. Proficiency in Python, and Java or Golang Experience with Agent platforms Expertise with ML/LLM frameworks such as PyTorch, TensorFlow, LangChain, or equivalent Experience with vector databases, orchestration frameworks, and modern MLOps practices Strong grounding in cloud-native architectures (AWS, GCP, Azure) Utilities experience a plus #J-18808-Ljbffr