AI Engineer I, II, III or Senior

Tri-State Generation and Transmission Association, Inc.•Westminster, CO

14h•$91,000 - $161,000

About The Position

Tri-State is hiring one AI Engineer at one of four levels: AI Engineer I, II, III, or Senior AI Engineer. The level will be determined based on the selected candidate’s qualifications and experience and Tri-State’s business needs at the time of hire. The role leads the development and delivery of AI solutions, partnering with business leaders to identify valuable opportunities, shape solutions, and guide work from discovery through production and ongoing operation. At the senior level, the position provides technical direction across teams, defines engineering standards and architectures for cloud and secure on-premises environments, and mentors engineers. It also evaluates emerging AI technologies and translates proven advances into responsible production capabilities. The position supports high-value utility applications such as load and renewable forecasting, asset health and predictive maintenance, wildfire and outage risk, and grid operations. It requires collaboration with business, data, infrastructure, cybersecurity, and other partners; sound technical and architectural judgment; business acumen; and the ability to build stakeholder alignment. AI solutions must follow Tri-State’s governance policies and account for safety, explainability, human oversight, security, privacy, regulatory requirements, and operational risk. Tri-State is a not-for-profit wholesale power supply cooperative serving electric distribution cooperatives and public power districts in Colorado, Nebraska, New Mexico, and Wyoming. It employs approximately 1,000 people across five states. The role may require regional travel of up to 10% and the ability to work overtime at any time of day or week is an essential function.

Requirements

Bachelor’s degree in Computer Science, Data Science, or a related field.
Expert knowledge of machine learning, deep learning, statistics, and data science, including experimental design, probabilistic forecasting, optimization, and model validation.
Expert knowledge of LLMs, agentic systems, RAG architectures, multi-agent orchestration, Model Context Protocol (MCP), and enterprise-scale generative AI application design.
Deep expertise in AI platform and solution architecture across cloud platforms and on-premises environments, including local model inference, GPU infrastructure, and Kubernetes.
Expert knowledge of MLOps and LLMOps, evaluation frameworks, observability, and AI cost management.
Deep understanding of AI security, privacy, and risk, including the OWASP Top 10 for LLM Applications, MITRE ATLAS, IT/OT segmentation, and model supply-chain risk.
Strong working knowledge of AI governance and risk management frameworks such as the NIST AI Risk Management Framework and ISO/IEC 42001.
Expert Python and software engineering skills, with the ability to prototype hands-on and review code and designs across teams.
Strong business acumen and stakeholder-management skills, including discovery facilitation, problem framing, business case development, and value measurement.
Ability to lead and align multiple teams and stakeholders through influence, technical credibility, and clear direction.
Exceptional written and verbal communication skills, including executive briefings, solution proposals, and presentations to non-technical audiences.
Eight or more years of experience delivering machine learning, data science, and AI solutions; the posting also states eight or more years with a Master’s degree or PhD.
Five or more years of experience as a technical lead, principal engineer, or architect leading multiple teams or projects.
Deep experience leading business engagement and delivering AI or ML solutions end to end, from opportunity identification through production and measurable business outcomes.
Experience delivering enterprise-scale AI or ML systems to production and operating them over their lifecycle.
Experience defining reference architectures or engineering standards adopted across teams.
Must possess a valid driver’s license; regional travel may be required up to 10%.

Nice To Haves

Master’s degree or PhD in Computer Science, Data Science, Statistics, or a related quantitative field.
One or more advanced AI certifications, such as Claude Certified Architect Pro, Microsoft Agentic AI Business Solutions Architect Expert, Databricks Gen AI Engineer, Databricks Certified Machine Learning Professional, or cloud architect certifications for Azure, AWS, or GCP.
Experience in the utility, energy, or another critical infrastructure industry, including grid operations, transmission, or asset management, and familiarity with NERC CIP.
Experience deploying AI in on-premises or air-gapped environments.
Expertise in specialized areas such as physics-informed machine learning, reinforcement learning, optimization, computer vision for infrastructure inspection, or geospatial analytics.
Published research, patents, conference presentations, or open-source contributions in AI or ML.
Experience building an AI community of practice or enterprise AI platform.

Responsibilities

Build and maintain relationships with business leaders and stakeholders, acting as the primary technical partner for AI initiatives.
Lead discovery with business units to identify problems and opportunities where AI, machine learning, and data science can create measurable value.
Frame business problems as AI solutions with clear success criteria, expected value, feasibility, and risk.
Develop business cases and prioritization recommendations for AI opportunities, considering value, effort, data readiness, and risk.
Set stakeholder expectations, communicate progress and results, and ensure solutions are adopted and achieve intended outcomes.
Lead end-to-end AI solution delivery across teams and projects, from discovery and design through build, deployment, adoption, and ongoing operation.
Lead the design of advanced AI and analytical solutions for utility use cases, including forecasting, asset health, predictive maintenance, wildfire and outage risk, and grid operations support.
Coordinate engineering work with other teams and data, infrastructure, cybersecurity, and business partners.
Measure and report business outcomes and drive improvements based on results and user feedback.
Define reference architectures, design patterns, and technical roadmaps for AI and machine learning solutions.
Architect AI platforms and shared services across cloud and on-premises environments, including model hosting, agent infrastructure, retrieval services, evaluation, and observability.
Lead technology and vendor evaluations, build-versus-buy analyses, and platform recommendations, including on-premises AI infrastructure needs.
Determine where AI workloads should run based on data sensitivity, regulatory requirements, latency, cost, and operational risk.
Guide teams on machine learning, deep learning, generative AI, statistics, data science, modeling approaches, experimental design, and interpretation of results.
Set standards for model validation, statistical rigor, uncertainty quantification, bias testing, and reproducibility.
Define AI engineering standards, including LLMOps and MLOps practices, evaluation frameworks, release gates, security patterns, and production-readiness criteria.
Review technical designs for high-risk, operationally adjacent, and enterprise-wide AI solutions.
Lead resolution of critical AI production issues and drive post-incident reviews and lasting improvements.
Turn emerging research and technology into production-ready capabilities through prototypes and pilots.
Ensure AI solutions comply with Tri-State’s AI governance policy and principles, with human-in-the-loop design, explainability, and safety controls appropriate to operational and regulatory risk.
Provide technical input to AI governance standards, risk assessments, AI security, and regulatory compliance, including NERC CIP.
Lead, guide, and mentor engineers across multiple AI teams and projects; contribute to interviews, skill development, and engineering best practices.
Share knowledge through the AI Community of Practice, technical presentations, and documentation.
Represent Tri-State’s AI engineering work in industry forums, peer utility groups, and engagements with vendors and academic partners.
Work overtime at any time of day or week as needed to support the obligation to provide continuous, reliable electric service.
Comply with company policies and procedures and maintain knowledge of applicable regulations, laws, standards, and best practices.

Benefits

Medical insurance
Dental insurance
Vision insurance
Flexible work schedules, including compressed work week and telecommuting opportunities to work remotely up to 40%
Health Savings Account (HSA)
Flexible Spending Accounts (FSA)
Tuition reimbursement
Life insurance
Retirement option
Long Term Disability (LTD)
Short Term Disability (STD)
Employee Assistance Program (EAP)
Paid leave benefits