AI Solutions Engineer

tdecu; United States; On-site; USD 120,000 - 180,000; Full time

Job summary

TDECU seeks an AI Solutions Engineer who embeds with business units to discover high-value automation opportunities and translate them into AI-powered prototypes. You’ll work on Python, SQL, APIs, and AI agents to ship first versions in weeks, not quarters.

You’ll rotate to new business domains, ensure governance and security, and hand off solutions to platform engineering as you scale the AI platform across the organization.

Qualifications

• Bachelor's degree in Computer Science, Information Systems, Data Science, AI, or related field.
• Experience building AI-powered software and prototypes with AI agents; production-acceptance processes.
• Proficiency in Python, SQL, and API integration.

Responsibilities

• 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 elevate risks early

Skills

Python

SQL

APIs

Education

Bachelor's degree in Computer Science, Information Systems, Data Science, Artificial Intelligence, or related field

Tools

Azure AI services

GitHub Copilot

Claude Code

Job description

Position Summary

Th e 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.

The ideal candidate is an AI-native builder: someone who uses AI agents as their primary way of building software, keeps up with model and tooling releases as they happen, and experiments with new AI capabilities on their own time. They are technically sound, with enough depth in Python, SQL, and APIs to verify, debug, and harden what AI produces, and they are equally comfortable in a room of business stakeholders, translating a messy process description into a working prototype and demoing it back.

Solutions built by the AI Solutions Engineer graduate through a production-acceptance process to the team's platform engineering function for long-term operation; the engineer then rotates to the next business deployment. The role offers unusual breadth: every few weeks brings a new business domain, a new problem, and a new build, backed by an established platform (orchestration, cloud deployment patterns, shared knowledge base) that makes each deployment faster than the last.

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 elevate risks early

Minimum Qualifications

Education

• 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

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

• Candidateshouldpossess3‑5yearsofdirectexperiencebuildingsolutions.
• 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