AI Solutions Engineer

AI Solutions Engineer *****Please NOTE: Client does not have Visa sponsorship capabilities . Must be local to Houston and able to work FT direct hire without the need of Visa assistance on a w2 Location: Houston, TX (Full-Time) Hybrid onsite 3x a week Industry: Financial Services / Credit Union Reports To: Head of Technology / VP of Engineering PRO TIP: Aside from the AI tech skills, the next most important thing we want to have highlight ed in the resume is the client facing experience , as this person will also be a technical Liaison for the organization. About the Role We are seeking a hands-on AI/ML & MLOps Engineer to join our newly formed AI team. You will leverage modern AI coding tools to rapidly prototype, build, and deploy production-ready AI/ML solutions in a highly regulated financial environment. The ideal candidate balances fast-paced prototyping ("vibe coding") with strict engineering discipline—ensuring our AI systems are secure, scalable, reliable, and compliant. Key Responsibilities AI & ML Engineering: Design, test, and deploy AI/ML solutions (LLMs, GenAI, RAG, agents, vector databases, and traditional ML) across internal workflows and member services. MLOps & Infrastructure: Build CI/CD pipelines, establish model lifecycle processes, manage versioning, and implement model observability (drift, latency, accuracy, cost). Rapid Prototyping: Turn business ideas into working proofs-of-concept quickly using AI-assisted coding tools (e.g., Cursor, GitHub Copilot, Claude Code) while maintaining production standards. Software Engineering: Develop Python-based APIs, microservices, and integrations across cloud services, databases, and enterprise platforms. Security & Governance: Safeguard member data by embedding strict security practices (access controls, prompt injection safeguards, data privacy, and compliance documentation). Qualifications Required 2+ years in software engineering, ML engineering, DevOps, or data engineering. Core Tech: Strong Python , Docker, Git, CI/CD, and REST API/microservices architecture. Cloud & MLOps: Hands-on experience with cloud infrastructure (AWS, Azure, or GCP) and model deployment/monitoring tools. AI & LLM Stack: Hands-on experience with GenAI, RAG architectures, prompt engineering, vector databases, and frameworks (e.g., LangChain, LlamaIndex, MLflow). Mindset: Strong ability to rapidly prototype with AI tools, review AI-generated code critically, and solve problems resourcefully. Nice to Have Experience in fintech, banking, credit unions, or regulated industries (familiarity with core platforms like Fiserv, Jack Henry, or Corelation). Experience with enterprise AI governance, model-risk management, and secure agent development. Degree in Computer Science, Data Science, or equivalent practical experience. Show more Show less