Job SummaryThe AI Engineer will play a critical role in designing, delivering, and scaling practical AI solutions across TASI Measurement. The role will build on our existing AI successes while identifying new opportunities that create measurable value across our business units.
Acting as both a hands-on technical practitioner and a cross-functional delivery lead, the AI Engineer will partner with business and IT stakeholders to translate operational challenges into secure, scalable AI-enabled solutions. This role is suited to someone who combines practical AI engineering capability with business understanding and enjoys taking solutions from initial discovery through deployment, adoption, and continuous improvement.
This position follows a hybrid schedule, requiring in-office attendance 2-3 days per week. Candidates must reside within commuting distance of our Largo, FL office and be able to meet this schedule consistently.Essential FunctionsOpportunity Discovery and Prioritization- Work directly with OpCos to understand business processes, challenges, and opportunities for AI-driven improvement.
- Identify, evaluate, and prioritize AI and automation opportunities based on strategic alignment, feasibility, risk, and potential business value.
- Facilitate discovery sessions and translate business requirements into clear technical designs, delivery plans, and success measures.
- Establish a named business owner, baseline measures, expected benefits, data requirements, and an adoption plan before initiatives move into delivery.
Solution Engineering and Delivery- Design, develop, test, and deploy AI-enabled applications, LLM solutions, intelligent automations, analytics solutions, and supporting integrations.
- Develop prototypes and proofs of concept, then work with internal teams and external partners to productionize successful solutions.
- Integrate AI solutions with enterprise applications, APIs, data platforms, and workflow tools where appropriate.
- Lead or coordinate end-to-end delivery, including discovery, architecture, development, testing, deployment, operational handoff, and post-launch optimization.
- Apply appropriate source control, documentation, testing, security, and application lifecycle practices throughout delivery.
Evaluation, Monitoring, and Value Measurement- Establish evaluation approaches for accuracy, reliability, security, performance, and business effectiveness.
- Monitor deployed solutions and improve them using technical performance, user feedback, adoption data, and measured business results.
- Validate AI outputs and identify risks such as inaccurate, inconsistent, biased, or unsupported responses.
- Define baseline measures, adoption metrics, expected benefits, and post-implementation results with business owners to document realized value.
- Create dashboards and concise reporting that communicate delivery progress, adoption, risks, and outcomes to technical and business stakeholders.
Adoption, Governance, and Continuous Improvement- Facilitate workshops, demonstrations, and training to increase AI literacy, responsible use, and adoption across the organization.
- Contribute to AI governance frameworks, standards, operating procedures, and the ongoing review of AI tools and use cases.
- Ensure appropriate technical documentation, data handling, privacy considerations, security controls, and risk mitigation practices.
- Evaluate AI tools, platforms, vendors, and implementation partners to support sustainable innovation.
- Stay current on emerging AI technologies and assess their practical relevance for TASI Measurement.
QualificationsRequired- Bachelor's degree in computer science, data science, analytics, information systems, engineering, or equivalent practical experience.
- Practical experience developing AI, automation, analytics, or software solutions.
- Hands-on experience with Python, APIs, SQL, data preparation, and modern development practices.
- Experience developing solutions using LLM platforms such as Azure OpenAI, OpenAI APIs, or comparable technologies.
- Working knowledge of prompt engineering, retrieval-augmented generation, agentic workflows, solution evaluation, and monitoring.
- Demonstrated ability to translate business requirements into technical designs and deployable solutions.
- Experience taking at least one AI, automation, analytics, or software solution from discovery through implementation.
- Strong communication skills, including the ability to explain technical concepts, risks, and trade-offs to non-technical stakeholders.
- Experience contributing to or leading cross-functional technology initiatives.
Preferred- Experience with Microsoft Azure, Azure AI Foundry, Azure OpenAI, Microsoft Copilot Studio, Power Platform, or comparable enterprise AI platforms.
- Experience with source control and collaborative development practices using GitHub or Azure DevOps.
- Experience integrating AI solutions with enterprise systems, APIs, and data sources.
- Familiarity with cloud architecture, identity and access management, security controls, and application lifecycle management.
- Experience working with external implementation partners or technology vendors.
- Experience in a multi-OpCo or decentralized business environment.
- Background in process improvement, automation, or digital transformation.
- Familiarity with AI governance, responsible AI principles, and enterprise IT architectures.
Key CompetenciesPractical engineering mindset: Builds reliable solutions while balancing speed, scalability, maintainability, and risk.
Business-oriented: Focuses technical effort on outcomes, efficiency, adoption, and measurable value.
Analytical thinking: Breaks complex problems into clear requirements, data needs, technical choices, and testable outcomes.
Curiosity and innovation: Explores emerging technologies and applies them selectively to real business needs.
Leadership without authority: Influences and guides teams through expertise, collaboration, and clear recommendations.
Clear communication: Makes complex AI concepts accessible to technical and non-technical audiences.
Change leadership: Helps teams adopt new tools, processes, and ways of working.
Accountability: Takes ownership from initial discovery through operational handoff and measured results.
Success in This Role Looks Like- AI and automation solutions delivered securely, reliably, and with clear business ownership.
- Initiatives supported by baselines, adoption measures, and documented ROI or operational improvement.
- Increased AI adoption, trust, and literacy across Operating Companies.
- Scalable, well-governed solutions embedded in daily operations rather than remaining as isolated pilots.
- Strong cross-functional relationships and repeat engagement from business teams.
- A prioritized AI roadmap that supports TASI Measurement's multi-year IT strategy.
- Reduced dependency on individual contributors through clear documentation, support models, and knowledge transfer.
Commitment to People and PlanetTASI Measurement is committed to fostering a sustainable and socially responsible environment. We believe that success is measured not only by financial results, but also by the positive impact we have on our employees, communities, and the world around us.
We strive to provide a supportive and inclusive workplace where every individual is valued, respected, and given equal opportunities to thrive. We prioritize the well-being, safety, and personal development of our employees, recognizing that they are the engine driving our success.
