AI Engineer I, II, III or Senior

Tri-State Generation and Transmission Association•Westminster, CO

10d•Hybrid

About The Position

Tri-State is hiring one AI Engineer at one of four levels: AI Engineer I (job grade 8), AI Engineer II (job grade 9), AI Engineer III (job grade 10), or Senior AI Engineer (job grade 11). The selected level will depend on the candidate’s qualifications and experience and Tri-State’s business needs at the time of hire. At the senior level, the AI Engineer leads AI engineering across multiple teams and projects, partnering with business leaders to identify valuable AI opportunities and deliver solutions from discovery through production and ongoing operation. The role sets technical direction and engineering standards for AI solutions across cloud and secure on-premises environments, evaluates emerging technology, and mentors engineers. Solutions are expected to be developed responsibly, with appropriate governance, safety, and human oversight. The position focuses on enterprise AI and machine learning, including utility applications such as load and renewable forecasting, asset health and predictive maintenance, wildfire and outage risk, and grid operations support. The role also contributes to AI governance, security, regulatory compliance, and business-outcome measurement. Regional travel may be required up to 10%, and a valid driver’s license is required. The job description states that 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 generative AI application design.
Deep expertise in AI platform and solution architecture across cloud platforms (Azure, AWS, or GCP; Databricks) and on-premises environments, including local model inference, GPU infrastructure, and Kubernetes.
Expert knowledge of MLOps and LLMOps, evaluation frameworks (including golden test sets, LLM-as-judge, and red-teaming), 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, including 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, including executive briefings, solution proposals, and presentations to non-technical audiences.
At least eight 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.
At least five 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.
Valid driver’s license.
Ability to work overtime at any time of day or week, identified as an essential function.

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 AI or ML research, patents, conference presentations, or open-source contributions.
Experience building an AI community of practice or enterprise AI platform.

Responsibilities

Build and maintain relationships with business leaders and stakeholders, serving as the primary technical partner for AI initiatives.
Lead business-unit discovery to identify problems and opportunities where AI, machine learning, and data science can create measurable value.
Frame business problems as AI solutions with defined success criteria, expected value, feasibility, and risk.
Develop business cases and prioritization recommendations that account for value, effort, data readiness, and risk.
Set stakeholder expectations, communicate progress and results, and promote adoption and intended outcomes of delivered solutions.
Lead end-to-end delivery of AI solutions across teams and projects, from discovery and design through build, deployment, adoption, and ongoing operation.
Lead design of AI and analytical solutions for utility use cases, including forecasting, predictive maintenance, wildfire and outage risk, and grid operations support.
Coordinate engineering across teams and with data, infrastructure, cybersecurity, and business partners.
Measure and report solution outcomes and drive improvements based on results and user feedback.
Define reference architectures, design patterns, and the technical roadmap 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 infrastructure needs.
Determine AI workload placement 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 solutions meet Tri-State AI governance policies and principles, including appropriate human-in-the-loop design, explainability, and safety controls.
Provide technical input to AI governance standards, risk assessments, AI security, and regulatory compliance, including NERC CIP.
Lead, guide, and mentor engineers across 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 such as EPRI, and with vendors and academic partners.
Maintain compliance with company policies and procedures and stay knowledgeable about applicable regulations, laws, standards, and best practices.
Work overtime at any time of day or week when necessary to support continuous, reliable electric service.

Benefits

Medical insurance
Dental insurance
Vision insurance
Flexible work schedules, including a 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