AI Solutions Engineer

Texas Dow Employees Credit Union; United States; On-site; USD 120,000 - 180,000; Full time

Job summary

Texas Dow Employees Credit Union seeks an AI Solutions Engineer to embed with Card Operations, Fraud, Lending, and the Contact Center to identify automation opportunities and ship AI-powered prototypes rapidly.

The role blends frontline development with platform integration, using AI agents, Azure AI, Python, and APIs to produce first versions within weeks while upholding governance, security, and production readiness.

Qualifications

• Bachelor's degree in CS/IS/DS/AI or equivalent
• 3–5 years of direct experience building AI-enabled solutions
• Experience with Python, SQL, and API integration
• 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 and live demos
• Ability to ship a working first version quickly and iterate with users
• Data privacy and security considerations in a regulated financial environment

Responsibilities

• Embed with Business Units – Deploy into a business department for a rotation and own the working relationship.
• Discover & Qualify Use Cases – Identify high-value AI opportunities and size benefits.
• Rapidly Prototype – Turn use cases into working prototypes with real data.
• Ship Working v1 Solutions – Deliver first working version and drive adoption.
• Verify AI Output – Ensure accuracy and compliance to governance.
• Build to Production Standards – Meet security and reliability requirements.
• Hand Off & Support – Document, train, and support post-launch.
• Reuse & Extend the Platform – Improve reusability for future deployments.
• Stay Ahead of AI Releases – Evaluate new models and tools hands-on.
• Communicate Across Levels – Report progress and risks clearly.
• Uphold AI Governance – Ensure compliance with data privacy and security policies.

Skills

AI coding agents

Python

SQL

LLM APIs

REST APIs

Azure cloud

Automation scripting

Data privacy

Stakeholder outreach

Education

Bachelor's degree in CS/IS/DS/AI or related field

Tools

Claude Code

GitHub Copilot

Cursor

Kestra

UiPath

Azure AI Services

Job description

Position Summary

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. 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, 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

Candidate should possess 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.

Physical Demands and Work Environment

(The physical demands and work environment characteristics described herein are representative of those that must be met by an employee to successfully perform essential