Senior Machine Learning Engineer
At a glance
Senior Machine Learning Engineering role at Q2Ebanking. Austin · full-time · no salary stated · we estimate $181,150–$247,500 across the United States.
Q2Ebanking has not published a salary for this role. The figure below is our estimate, not theirs.
aimlroles summary, based on the employer's posting.
What you'll do
- Build and tune production LLM agents using retrieval, memory, prompt design, tool calls and multi-step reasoning
- Create evaluation pipelines that track agent accuracy, task completion, speed, cost, safety and hallucinations
- Connect agent workflows to vector stores, external platforms, internal APIs and microservices for retrieval and actions
- Adapt domain-specific language models, then deploy secure AI services with monitoring and reliable release pipelines
- Review live system metrics, address data drift and feedback, and improve models and prompts with product, data and engineering teams
What you bring
- At least five years in software, machine learning or AI engineering, including responsibility for production systems
- Python proficiency and practical experience building modern LLM applications, retrieval-augmented generation and agent workflows
- A relevant degree with five years of related work, an advanced degree with three years, or equivalent hands-on experience
- You communicate fluently in written and spoken English and are authorized to work for any U.S. employer
Who this fits
This senior role suits an engineer who can move between models, backend services, cloud systems, data pipelines and application interfaces. You should be comfortable taking ownership and setting practices amid changing technical problems. The position requires U.S. work authorization; visa sponsorship is unavailable.
From the employer
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 through our year-round Q2 ChangeMakers awards program and global moments of recognition and connection. We invest in the growth and development of our team members through ongoing learning opportunities, internal mobility, and meaningful leadership relationships. We also know that nothing builds trust and collaboration like having fun and giving back together. From company-wide volunteer days to events like our Q2 Homecoming Week—featuring learning, community service, and culture-building experiences—we create opportunities to connect, grow, and make an impact.
The Job At-A-Glance:
Join a fast-growing AI engineering team building the next generation of intelligent products for the financial services industry. As a Machine Learning Engineer, you'll design and deliver production-ready AI solutions, develop the tools, APIs, and backend services that enable them, and help shape the future of our AI platform. From evaluation frameworks, deployment pipelines, and microservice-based AI capabilities to next-generation agentic workflows, you'll have the opportunity to work across the stack, prototype new ideas, and turn innovation into real customer value. We're looking for engineers who are passionate about technology, committed to building high-quality, enterprise-grade software, and who thrive in collaborative environments. The ideal candidate stays current with the rapidly evolving AI landscape, enjoys solving meaningful problems, and is excited to help build innovative solutions alongside a team that values creativity, ownership, continuous learning, and shipping great products.
A Typical Day:
- Build, test, and optimize production-ready LLM agents leveraging multi-step reasoning, prompt engineering, retrieval, memory, and tool-calling capabilities.
- Develop scalable evaluation pipelines (Evals) to continuously measure accuracy, task completion, latency, cost efficiency, safety, and hallucination rates.
- Integrate agentic workflows with external platforms, vector databases, microservices, MCP, and internal APIs to enable reliable context retrieval and automated action-taking.
- Fine-tune domain-specific LLMs using modern adaptation techniques like supervised fine-tuning (SFT), LoRA/PEFT, and preference optimization to maximize performance.
- Productionize AI services across cloud and backend architecture, implementing security guardrails, observability, and robust deployment pipelines.
- Track production metrics to identify data drift, manage feedback loops, implement retuning strategies, and systematically upgrade model and prompt performance.
- Partner with product, data, and engineering teams to prototype emerging AI tech, navigate technical tradeoffs, and scale early-stage concepts into enterprise-grade software.
Bring Your Passion, Do What You Love. Here’s What We’re Looking For:
- Bachelor’s degree in a relevant field with 5+ years of related experience, an advanced degree with 3+ years of experience, or equivalent practical work experience.
- 5+ years of professional software/ML/AI engineering experience with demonstrated ownership of production systems. Proficiency in Python is required, alongside experience with backend languages (such as C#, Java, TypeScript) to build robust APIs, microservices, and data integrations.
- Hands-on experience developing modern AI patterns, including LLM applications, retrieval-augmented generation (RAG), agentic workflows, multi-step reasoning, and model/tool integration.
- Proven track record building and operating AI/ML systems in production, including model evaluation frameworks (Evals), monitoring, safety guardrails, continuous retuning, and observability.
- Experience deploying and scaling cloud-native infrastructure, containers, and automated pipelines. Familiarity with Docker, Kubernetes, and MLOps practices is highly valued.
- Ability and willingness to work across the full stack—bridging AI models, backend services, cloud infrastructure, data pipelines, and application interfaces.
- Strong sense of ownership, technical curiosity, and comfort navigating ambiguity to establish best practices in an evolving AI landscape.
This position requires fluent written and oral communication in English.
Applicants must be authorized to work for any employer in the U.S. We are unable to sponsor or take over sponsorship of an employment Visa at this time.
Health & Wellness
Hybrid Work Opportunities
Flexible Time Off
Career Development & Mentoring Programs
Health & Wellness Benefits, including competitive health insurance offerings and generous paid parental leave for eligible new parents
Community Volunteering & Company Philanthropy Programs
Employee Peer Recognition Programs – “You Earned it”
Click here to find out more about the benefits we offer.
Our Culture & Commitment:
We’re proud to foster a supportive, inclusive environment where career growth, collaboration, and wellness are prioritized. And our benefits go beyond healthcare—offering resources for physical, mental, and professional well-being. Click here to find out more about the benefits we offer. Q2 employees are encouraged to give back through volunteer work and nonprofit support through our Spark Program (see more). We believe in making an impact—in the industry and in the community.
We are an Equal Opportunity Employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender 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