AI Engineer I, II, III or Senior

The Senior AI Engineer leads AI engineering at Tri-State, guiding multiple engineering teams and projects from business opportunity through production delivery. A domain expert in artificial intelligence, machine learning, and data science, this role partners with business leaders to identify where AI can create the most value, shapes the solutions, and leads their delivery end to end. Because AI technology is evolving rapidly, the Senior AI Engineer continuously evaluates emerging models, tools, and techniques and brings proven advances into Tri-State's solutions responsibly.

The Senior AI Engineer sets the technical direction and engineering standards for AI solutions across cloud and secure on-premises environments, and mentors engineers across teams to build depth and capability. Success in this role requires deep expertise in AI and data science, strong business acumen, sound architectural judgment, and the ability to build trust and alignment with stakeholders at every level of the organization.

Note: There is one position available, and this position will be filled at one of four job grade 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. This decision will be based on the qualifications and experience of the selected candidate and Tri-State business needs at the time of hire.

Tri-State recognizes the value of a highly engaged and committed workforce and provides an excellent benefits program that includes : 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) and Paid Leave Benefits.

Senior AI Engineer

Hiring Salary Range: $125,000-$161,000

AI Engineer III

Hiring Salary Range: $113,000-$143,000

AI Engineer II

Hiring Salary Range: $101,000-$128,000

AI Engineer I

Hiring Salary Range: $91,000-$114,000

Actual compensation offer to candidate may vary outside of the posted hiring salary range based upon work experience, education and/or skill level.

• Build and maintain strong relationships with business leaders and stakeholders across Tri-State, 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 into well-defined AI solutions with clear success criteria, expected value, feasibility, and risk.
• Build business cases and prioritization recommendations for AI opportunities, including value, effort, data readiness, and risk.
• Set stakeholder expectations, communicate progress and results, and make sure delivered solutions are adopted and achieve their intended outcomes.
• Lead end-to-end delivery of AI solutions across multiple teams and projects, from discovery and design through build, deployment, adoption, and ongoing operation.
• Lead the design of advanced AI and analytical solutions for high-value utility use cases, such as load and renewable forecasting, asset health and predictive maintenance, wildfire and outage risk, and grid operations support.
• Coordinate engineering work across teams and with data, infrastructure, cybersecurity, and business partners to deliver solutions on time and as designed.
• Measure and report the business outcomes of delivered solutions, 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 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.
• Serve as the subject matter expert in machine learning, deep learning, generative AI, statistics, and data science, and guide teams on 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 the technical designs of 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.
• Make sure AI solutions meet Tri-State's AI governance policy and principles, with human-in-the-loop design, explainability, and safety controls that fit each system's operational and regulatory risk.
• Provide technical input to AI governance standards, risk assessments, AI security, and regulatory compliance (e.g., NERC CIP).
• 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 across the team.
• Share knowledge across Tri-State through the AI Community of Practice, technical presentations, and documentation.
• Represent Tri-State's AI engineering work in industry forums, peer utility groups (e.g., EPRI), and with vendors and academic partners.
• Because Tri-State has an obligation to provide continuous, reliable electric service to its customers, the ability to work overtime at any time of the day or week is considered an essential function of the job.
• Maintain compliance with all company policies and procedures and attain knowledge and remain knowledgeable of regulations, laws, standards, and best practices applicable to functional area.

Education and Training:

• Bachelor's degree in Computer Science, Data Science, or a related field; Master's degree or PhD preferred.

Knowledge, Skills, and Ability:

• 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 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, including executive briefings, solution proposals, and presentations to non-technical audiences.

Experience:

• 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.

• 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.

Other:

• Regional travel may be required by up to 10% as needed. (Must possess a valid driver’s license.)

DESIRED JOB QUALIFICATIONS

• 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 (Azure, AWS, 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 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.

Note: The above requirements describe the experience and education qualifications for the Senior AI Engineer. Those with less experience will be hired at the I, II or III job grade level.