Senior AI Engineer - Production ML & Cloud Pipelines

Q2; United States; Hybrid; USD 130,000 - 170,000; Full time

Benefits offered by this job

Hybrid Work Opportunities

Flexible Time Off

Career Development & Mentoring Program

Job summary

Q2 is seeking a Machine Learning Engineer to design and deliver production-ready AI capabilities, APIs, and backend services that enable real-world financial solutions. You will work across the stack, prototype ideas, and translate research into customer value.

You will collaborate with product, engineering, data, and platform teams, stay current with the AI landscape, and own parts of the development lifecycle from evaluation to deployment.

Qualifications

• Bachelor's degree in CS or related field is required or equivalent experience.
• 5+ years in software/ML/AI engineering with production ownership.
• Strong Python and backend service experience.

Responsibilities

• Design, build, and deliver production-ready AI capabilities and APIs.
• Develop scalable APIs, microservices, data integrations, and backend systems for AI-powered products.
• Build and improve AI infrastructure including evaluation frameworks, deployment pipelines, monitoring, and testing.
• Prototype and evaluate emerging AI technologies and turn ideas into enterprise-grade solutions.
• Collaborate with product, engineering, data, and platform teams across cloud, data, and application layers.

Skills

Python

LLMs / AI

Cloud infrastructure

Ownership & collaboration

Education

Bachelor's degree in Computer Science or related field

Tools

Kubernetes

Docker

MLOps tooling

Job description

Q2 is seeking a Machine Learning Engineer to design and deliver production-ready AI capabilities, APIs, and backend services that enable real-world financial solutions. You will work across the stack, prototype ideas, and translate research into customer value.

You will collaborate with product, engineering, data, and platform teams, stay current with the AI landscape, and own parts of the development lifecycle from evaluation to deployment.