Q2

Financial Technology

MachineLearningEngineer

$155–215k ~AI est. Austin, Texas, United States FULL TIME Remote Friendly
Market Sentiment
HIGH DEMAND

Neural analysis suggests this role is
optimal for Mid+ candidates.

The Brief

“Machine Learning Engineer at Q2. Skills: AI systems, Architecture patterns, LLMs, Cloud platforms. Design enterprise solution patterns. Align business needs with scalable implementation”

Industry & Context.

Financial Technology
Problems you'll solve

Analyze and resolve gaps; Evaluate ambiguous problems

Eligibility Requirements

Minimal travel

What They're Looking For.

Must Have

5–8 years of relevant professional experience, Bachelor’s degree in a relevant field, Understanding of enterprise architecture principles, Understanding of system design, Understanding of integration patterns, Understanding of scalable delivery models, Experience partnering with business and technical stakeholders, Ability to evaluate ambiguous problems, Ability to structure recommendations, Ability to communicate trade-offs clearly, Demonstrated judgment in balancing innovation, security, governance, cost, and operational feasibility, Working knowledge of responsible technology adoption, Working knowledge of risk management, Working knowledge of enterprise control environments, Fluent written and oral communication in English

Nice to Have

Experience with LLMs, Experience with APIs, Experience with RAG, Experience with vector search, Experience with intelligent agents, Experience with orchestration workflows, Experience with Snowflake, Experience with AWS, Experience with Azure, Experience with enterprise data integration

What You'll Do.

Design enterprise solution patterns

Align business needs with scalable implementation

Collaborate with cross-functional teams

Translate business challenges into architecture recommendations

Identify gaps related to scalability

Analyze gaps related to data readiness

Resolve gaps related to interoperability

Resolve gaps related to operational adoption

Define reusable frameworks

Define standards that improve delivery consistency

Evaluate solution options

Provide recommendations balancing performance

Guide teams through architecture decisions

Support responsible adoption

Support long-term maintainability

Influence organizational direction

Promote best practices

Promote shared patterns

Promote practical governance approaches

Stay current on emerging practices

Assess applicability to enterprise priorities

How You'll Work.

Team & Collaboration

Cross-functional teams; Data teams; Engineering teams; Business applications teams; Operations teams; IAM teams; Governance teams

Communication Scope

Communicate trade-offs

Full 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, 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, planning, or stakeholder engagement. ## Responsibilities * Design enterprise solution patterns that align business needs with scalable implementation approaches. * Collaborate with cross-f

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