Principal AI Engineer

Job description

Ormat Technologies (NYSE: ORA) is a publicly traded renewable energy company and a global leader in geothermal and energy storage. THE ROLE Ormat's AI Center of Excellence is responsible for AI across the company – solutions, governance, enablement, adoption, and measurement. As Principal AI Engineer, you are the technical backbone of that mandate. You own the platforms our AI runs on, the internal marketplace through which teams discover and deploy approved AI capabilities, and the financial discipline that keeps AI consumption accountable as it scales. You are the senior technical authority and escalation point for a federated network of roughly forty AI Leads and business-unit builders. You set the standards they build against, review the complex work, and define what production-ready means before anything reaches users. You work alongside the AI governance function within the Center of Excellence and partner closely with IT on shared infrastructure. This is a hands-on principal role for someone who wants to shape how an industrial, operations-heavy business adopts AI, not just ship another model. What You'll Do AI platforms. Own the AI platform stack end to end – architecture, reliability, integration patterns, and observability - across Microsoft 365, Copilot Studio, Power Platform, and the LLM gateway and orchestration layers. Set the standards for how solutions are built, deployed, and monitored, with DevOps-grade lifecycle discipline: testing, evaluation, rollback, and ownership handoff. Complex use cases. Take the hardest, most ambiguous AI problems and turn them into buildable use cases: scoping them, making the technical stack decisions (language, framework, deployment), and building them yourself where needed. Most importantly, map who's needed for the task, pull in the right people, and guide them to delivery. You're the person who knows what "good" looks like and directs the build accordingly. You set the technical playbook for the AI Leads and builders, review their complex builds, and mentor them from configured agents toward custom development. AI marketplace. Build and manage the internal marketplace where employees discover, request, and deploy approved agents and solutions, with the vetting, access controls, and audit trails that keep everything published secure and compliant. AI FinOps. Own cost visibility, show-back, budgeting, and forecasting across LLM providers (token economics), cloud AI services, and SaaS AI tools. Establish cost-per-use-case and cost-per-agent metrics and drive optimization . Guardrails and controls. Define and implement the guardrails every AI solution runs behind - input/output controls, tool permissioning, and responsible-AI checks , in partnership with IT , before anything reaches users. Applied data science. Apply working data science fluency to evaluate model choices, review the data pipelines feeding agents, and validate builder output. This is a supporting capability within the role rather than a standalone research track. What We’re Looking for 8+ years in software, platform, ML, or data engineering, with recent depth in production AI and LLM systems. Proven ownership of a platform or infrastructure area: architecture, deployment, observability, and security. Hands-on experience with modern LLM application patterns - model gateways, RAG pipelines, agent runtimes, evaluation infrastructure, and prompt and version management. Cloud and AI cost management: budgeting, forecasting, anomaly detection, and cost attribution across token and cloud spend. Strong grounding in AI governance and security - role-based access, audit logging, tool permissioning, and responsible-AI controls. Working data science and ML fluency sufficient to evaluate models and data pipelines. Proficiency in Python and modern deployment tooling: containers, CI/CD, and infrastructure as code. A track record of mentoring engineers and setting standards that other teams actually adopt. The communication skills to translate technical decisions for both builders and senior leadership. A plus: experience in energy, manufacturing, or another operations-heavy industry; experience standing up an internal AI marketplace or agent platform; and the Microsoft AI stack (Copilot Studio, Power Platform) at enterprise scale.