Senior Machine Learning Engineer

• Design, build, and deliver production-ready AI capabilities, services, and applications
• Develop scalable APIs, microservices, data integrations, and backend systems for AI-powered products and workflows
• Build and improve AI infrastructure, including evaluation frameworks, deployment pipelines, monitoring, observability, and automated testing
• Prototype and evaluate emerging AI technologies, including LLMs and agentic workflows
• Turn successful AI prototypes into reliable, enterprise-grade solutions
• Work across cloud infrastructure, data platforms, and application layers
• Integrate AI capabilities into the broader Q2 technology ecosystem
• Collaborate with product, engineering, data, and platform teams
• Define solutions, make technical tradeoffs, and take ideas from exploration through production

• Bachelor’s degree in a relevant degree and a minimum of 5 years of related experience; or an advanced degree with 3+ years of experience; or equivalent related work experience
• 5+ years of professional software/ML/AI engineering experience, with meaningful production ownership
• Strong software engineering fundamentals and a track record of designing, building, deploying and maintaining production systems
• Strong Python proficiency
• Experience with backend/application languages such as C#, Java, or Type Script is valuable
• Experience building APIs, backend services, microservices, data integrations, or distributed systems
• Hands‑on experience with cloud infrastructure, containers, and production deployment
• Kubernetes/Docker/MLOPs experience is particularly valuable
• Experience building or operating ML/AI systems in production, including evaluation, monitoring, observability, and deployment pipelines
• Exposure to LLM applications, RAG, agentic workflows, model/tool integration, or similar modern AI patterns
• Ability and willingness to work across AI, backend, infrastructure, data, and occasionally UI/application code
• Strong ownership, curiosity, and comfort operating in an environment where patterns and solutions are still being established
• Fluent written and oral communication in English
• Authorized to work for any employer in the U.S.
• Employment visa sponsorship is unavailable

Demonstrates expertise in designing and deploying AI capabilities and production systems, with strong proficiency in Python and experience in cloud infrastructure, microservices, and ML/AI systems. Capable of collaborating across teams to integrate AI solutions into existing technology ecosystems.

• Python Proficiency
• AI Systems Development
• Cloud Infrastructure Experience
• Microservices Architecture
• Kubernetes/Docker/MLOps

• Software Engineering Fundamentals
• API Development
• Backend Development
• Data Integration
• Production Deployment
• Microservices
• Distributed Systems
• ML/AI Systems
• Evaluation Frameworks
• Automated Testing

• Strong Ownership
• Curiosity
• Effective Communication

• AI Capabilities
• LLM Applications
• Agentic Workflows
• Production Systems
• Technical Tradeoffs

• Kubernetes
• Docker
• Cloud Infrastructure
• MLOps
• Data Platforms