Emerging Technology Architect
AI summary of the role
This Machine Learning Engineer role at Q2 designs enterprise AI architectures and reusable frameworks for scalable, secure AI adoption across the organization.
No longer in the current catalog. Last included 2026-09-09. Check the employer’s posting for availability.
What you’ll do
- Design enterprise solution patterns that align business needs with scalable implementation approaches.
- Collaborate with cross-functional teams to translate business challenges into structured architecture recommendations.
- Define reusable frameworks and standards that improve delivery consistency across teams and use cases.
- Evaluate solution options, assess trade-offs, and provide recommendations that balance performance, cost, risk, and business value.
What you’ll bring
- 5–8 years of relevant professional experience in engineering, architecture, data, or enterprise technology roles.
- Bachelor’s degree in a relevant field.
- Strong understanding of enterprise architecture principles, system design, integration patterns, and scalable delivery models.
- Experience partnering with business and technical stakeholders to define practical solutions for complex organizational needs.
Technologies
LLMs · RAG · vector search · intelligent agents · Snowflake · AWS · Azure · APIs · IAM
About Q2
Cloud digital banking, lending, and risk/fraud platform for US and international banks, credit unions, alternative lenders, and fintechs.
Public · 2000–5000 people
Source and classification
Internal deployment & tooling · Evidence for this classification:
having fun. We hold an annual Dodgeball for Charity event at our Q2 Stadium in Austin, inviting other local companies to play, and community organizations we support to raise money and awareness together. Summary The Machine Learning Engineer designs and evolves enterprise AI systems and architectures that enable scalable, secure, and high-impact adoption across the organization. This role defines end-to-end AI solution patterns involving LLMs, APIs, RAG, vector search, intelligent agents, orchestration workflows, Snowflake, cloud platforms such as AWS and Azure, and enterprise data integration. The role partners across data, engineering, business applications, operations, IAM, and governance teams to create reusable frameworks that accelerate delivery while supporting security, access controls, compliance, and audit needs. This position may require minimal travel for collaboration,
More from the job description
As passionate about our people as we are about our mission. Why Join Q2? Q2 is a leading provider of digital banking and lending solutions to banks, credit unions, alternative finance companies, and fintechs in the U.S. and internationally. Our mission is simple: build strong and diverse communities through innovative financial technology—and we do that by empowering our people to help create success for our customers. What Makes Q2 Special? Being as passionate about our people as we are about our mission. We celebrate our employees in many ways, including our “Circle of Awesomeness” award ceremony and day of employee celebration among others! We invest in the growth and development of our team members through ongoing learning opportunities, mentorship programs, internal mobility, and meaningful leadership relationships. We also know that nothing builds trust and collaboration like having fun. We hold an annual Dodgeball for Charity event at our Q2 Stadium in Austin, inviting other local companies to play, and community organizations we support to raise money and awareness together. Summary The Machine Learning Engineer designs and evolves enterprise AI systems and architectures that enable scalable, secure, and high-impact adoption across the organization. This role defines end-to-end AI solution patterns involving LLMs, APIs, RAG, vector search, intelligent agents, orch [... source excerpt omitted ...] delivery while supporting security, access controls, compliance, and audit needs. This position may require minimal travel for collaboration, planning, or stakeholder engagement. Responsibilities Design enterprise solution patterns that align business needs with scalable implementation approaches. Collaborate with cross-functional teams to translate business challenges into structured architecture recommendations. Identify, analyze, and resolve gaps related to scalability, data readiness, interoperability, and operational adoption. Define reusable frameworks and standards that improve delivery consistency across teams and use cases. Evaluate solution options, assess trade-offs, [... source excerpt omitted ...] identity, national origin, age, disability, genetic information, or veteran status. Applicants in California or Washington State may not be exempt from federal and state overtime requirements
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