AI Solutions Engineer

Position Summary

The AI Solutions Engineer is a forward-deployed role: you embed directly with TDECU business teams such as Card Operations, Fraud, Lending, and the Contact Center to discover high-value automation opportunities and turn them into working AI-powered solutions, fast. This is a builder role at the front line: you sit with the business, learn their processes firsthand, and ship a working first version in weeks, not quarters, using AI-native development tools (AI coding agents such as Claude Code and GitHub Copilot), Azure AI services, Python, and APIs.

Responsibilities And Duties

• Embed with Business Units – Deploy into a business department for a defined rotation; own the working relationship with that team and represent the AI team inside their operation.
• Discover & Qualify Use Cases – Identify and scope high-value AI and automation opportunities from inside the business; size the expected benefit and feed a prioritized intake pipeline.
• Rapidly Prototype – Turn qualified use cases into working prototypes; own the feasibility answer for each, proving or disproving it quickly with real business data.
• Ship Working v1 Solutions – Deliver the first working version of each approved solution; own its adoption, with success measured by the business using it in daily operations.
• Verify AI Output – Own the accuracy of everything delivered; validate, test, and reconcile results to the standard expected of a regulated financial institution.
• Build to Production Standards – Ensure every solution meets the team's production-acceptance standards for security, reliability, and documentation; own each solution's readiness for handoff.
• Hand Off & Support – Deliver each completed solution to its business owner with documentation, training, and a defined support path; remain accountable for the solution's performance until it is formally accepted by the business owner and platform engineering.
• Reuse & Extend the Platform – Grow the team's shared platform and knowledge base with each deployment; own the reusability of what you build so every engagement starts further ahead than the last.
• Stay Ahead of AI Releases – Own the team's awareness of new models, tools, and open-source releases; evaluate them hands-on and recommend what TDECU adopts.
• Communicate Across Levels – Own communication for your deployments; keep business owners, the AI team, and leadership informed of progress, risks, and outcomes in terms each audience understands.
• Uphold AI Governance – Own compliance for every solution you build within TDECU's AI governance, data privacy, and security policies; identify and escalate risks early.

Education Minimum Qualifications

• Bachelor’s degree in Computer Science, Information Systems, Data Science, Artificial Intelligence, or a related field OR equivalent combination of education and work experience.

Certifications (Preferred But Not Required)

• AI & Cloud Certifications (Microsoft AI Engineer, Azure AI Fundamentals, Google AI, OpenAI certifications, etc.)
• Automation & Orchestration Platforms (Power Platform, Kestra, UiPath, or similar)
• Programming & Data Certifications (Python, SQL, Cloud Development, etc.)

Proof of AI Work (Accepted in Place of Traditional Experience)

• A portfolio of things you have actually built with AI, such as side projects, GitHub repositories, self-hosted tools, and personal automations.
• Hands-on use of AI coding agents (Claude Code, Cursor, GitHub Copilot) as a primary development workflow.
• Running, deploying, or fine-tuning open-source models and experimenting with new AI releases as they ship.
• Solving real-world business problems end-to-end using AI and automation.

Experience

• Minimum of 3-5 years of direct experience building solutions.
• Demonstrated ability to build and ship working software using AI-assisted development as the primary workflow (AI coding agents, LLM APIs, prompt and context engineering).
• Programming fundamentals in Python, SQL, and API integration, strong enough to review, debug, and verify AI-generated code rather than write everything from scratch.
• Cloud experience deploying and operating applications and services (Azure preferred; AWS or Google Cloud acceptable).
• Experience automating business processes using workflow tools, scripting, orchestration platforms, or RPA.
• Strong stakeholder-facing skills: requirements discovery, live demos, and iterating directly with non-technical business users.
• Track record of rapid prototyping: shipping a working first version quickly, then improving through iteration with users.
• A testing and verification instinct: data reconciliation, edge-case testing, and never shipping unverified AI output.
• Evidence of staying current with AI by following model and tooling releases and experimenting with them hands-on.
• An ownership mindset: takes a problem end-to-end from discovery through handoff without waiting for detailed specifications.
• Ability to operate in a regulated financial environment with strong judgment around data privacy and security.

Knowledge, Skills, And Abilities

• AI-Native Development: AI coding agents, prompt and context engineering, LLM APIs, rapid prototyping.
• Programming & Integration: Python, SQL, REST APIs, automation scripting.
• Cloud & Security: Azure services (AI, Functions, App Service, SQL), deployment and security best practices.
• Business & Communication: use-case discovery, effective demos, translating business needs into working software.
• Judgment & Verification: testing AI output, data reconciliation, and risk awareness appropriate to financial services.