AI Engineer I, II, III or Senior

Tri-State Generation and Transmission Association Inc. is seeking an AI Engineer at one of four job grade levels: AI Engineer I, AI Engineer II, AI Engineer III, or Senior. This hybrid role is based in Westminster, CO, and focuses on leading AI engineering work from business opportunity through production delivery, including platform direction and standards across cloud and secure on-premises environments.

The selected candidate will partner closely with business leaders and stakeholders to identify measurable AI value, translate business problems into well-defined AI solutions, and coordinate delivery across engineering teams, data, infrastructure, and cybersecurity. This position also supports utility use cases such as forecasting, asset health, and operational risk, with an emphasis on governance, safety controls, and adoption of outcomes.

Role scope

• Serve as the primary technical partner for AI initiatives by building and maintaining strong relationships with business leaders and stakeholders.
• Lead discovery with business units to identify problems and opportunities where AI, machine learning, and data science can create measurable value.
• Frame business problems into AI solutions with clear success criteria, expected value, feasibility, and risk.
• Build business cases and prioritization recommendations, including value, effort, data readiness, and risk.
• Set expectations, communicate progress and results, and ensure delivered solutions are adopted and achieve intended outcomes.
• Lead end-to-end delivery of AI solutions across multiple teams and projects, including discovery and design through build, deployment, adoption, and ongoing operation.

AI solution and platform leadership

• Lead design of advanced AI and analytical solutions for high-value utility use cases, including load and renewable forecasting, asset health and predictive maintenance, wildfire and outage risk, and grid operations support.
• Coordinate delivery work across teams and with 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, including build-versus-buy analysis and platform recommendations, including on-premises AI infrastructure needs.
• Determine where AI workloads run based on data sensitivity, regulatory requirements, latency, cost, and operational risk.

ML, LLM, governance, and engineering standards

• Act as a subject matter expert in machine learning, deep learning, generative AI, statistics, and data science, including modeling approaches, experimental design, and result interpretation.
• 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 meet Tri-State AI governance policy and principles, including human-in-the-loop design, explainability, and safety controls aligned to each system’s operational and regulatory risk.
• Provide technical input to AI governance standards, risk assessments, AI security, and regulatory compliance (including NERC CIP).

Leadership, communication, and community

• Lead, guide, and mentor engineers across multiple AI teams and projects to build technical depth and delivery capability.
• Contribute to technical interviews, skill development, and AI engineering best practices.
• Share knowledge through Tri-State’s AI Community of Practice, technical presentations, and documentation.
• Represent Tri-State’s AI engineering work in industry forums, peer utility groups (including EPRI), and with vendors and academic partners.

Essential functions

• Because Tri-State has an obligation to provide continuous, reliable electric service, the ability to work overtime at any time of the day or week is an essential function of the job.
• Maintain compliance with company policies and procedures, and stay knowledgeable of applicable regulations, laws, standards, and best practices.

Required qualifications

• Eight (8) or more years of experience delivering machine learning, data science, and AI solutions, or eight (8) years or more with a Master's degree or PhD.
• Including Five (5) or more years of experience as a technical lead, principal engineer, or architect leading multiple teams or projects.
• 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 generative AI application design at enterprise scale.
• Deep expertise in AI platform and solution architecture across cloud (Azure/AWS/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 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.
• Bachelor’s degree in Computer Science, Data Science, or a related field; Master’s degree or PhD preferred.

Technologies

• Python, Azure, AWS, GCP, Databricks, Kubernetes
• LLMs, RAG architectures, Model Context Protocol (MCP), LLMOps, MLOps
• OWASP Top 10 for LLM Applications, MITRE ATLAS
• NIST AI Risk Management Framework, ISO/IEC 42001
• NERC CIP

Travel and licensure

• Regional travel may be required up to 10% as needed (a valid driver's license is required).

Compensation

• Salary range (annual): USD 91,000 - 161,000
• AI Engineer I: $91,000-$114,000
• AI Engineer II: $101,000-$128,000
• AI Engineer III: $113,000-$143,000
• Senior AI Engineer: $125,000-$161,000

This posting reflects one position available, to be filled at one of four job grade levels. The selected level will be based on qualifications and experience, and on Tri-State business needs at the time of hire. The experience and education requirements listed describe the Senior AI Engineer; candidates with less experience may be hired at the I, II, or III level.

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 Assistant Program (EAP)
• Paid Leave Benefits

Desired qualifications

• Master’s degree or PhD in Computer Science, Data Science, Statistics, or a related quantitative field.
• Advanced AI certifications (examples listed include 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 critical infrastructure industry, including grid operations 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 ML, 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.