Senior Machine Learning Engineer

About the role

At Bluevine, we’re on a mission to empower small businesses with innovative financial solutions. In this role, you will take an integral part in designing the next-generation infrastructure that powers our data-driven products. You will help shape and build the platforms and production-grade solutions that enable our Data teams to deliver impactful AI/ML capabilities at scale. If you are passionate about turning complex data into reliable, scalable systems that solve real-world financial challenges, we want you on our team.

What You'll Do

• Build AI/ML Infrastructure: Design, develop, and maintain platforms and tooling that accelerate the entire AI lifecycle—from experimentation and training to deployment, observability, and governance.

• Productionize AI: Partner with our Data Science, Analytics, and Research teams to turn prototypes and notebooks into highly reliable, scalable production systems.

• Develop Modern AI Workflows: Build robust data and feature pipelines, and develop reusable components for GenAI capabilities like LLMs, embeddings, RAG, and agentic workflows.

• Champion Best Practices: Utilize orchestration tools, cloud AI services (such as AWS Bedrock, SageMaker, etc.), and modern frameworks to design first-in-class solutions for the financial sector.

What We Look For

• 5+ years of full-time Software Engineering experience in a fast-paced, data-centric environment, with strong proficiency in Python.
• 2+ years of hands-on experience in a Machine Learning Engineering, MLOps, ML Infrastructure, or hands-on, full-stack Data Science role.
• Deep understanding of the entire ML/DS lifecycle, from research and development to deployment.
• Proven hands-on experience in building production-grade solutions, platforms, or infrastructure for AI or ML.
• Experience building and deploying modern AI and LLM-based systems (prompt orchestration, embeddings, retrieval-augmented generation, and evaluation frameworks).
• Deep experience with cloud infrastructure (AWS preferred), including services like SageMaker, Bedrock, Lambda, and S3.
• Strong software engineering fundamentals, including system design, CI/CD, testing, code quality, and operational excellence.
• Exceptional communication skills, with the ability to collaborate smoothly and explain complex technical ideas clearly.
• Proactive, result-driven team player with very good English (written and verbal).

Bonus points if you also have:

• Experience with vector databases, advanced experiment tracking tools, or specialized ML observability platforms.
• Background in streaming, event-driven systems, and real-time analytics.
• Experience with responsible AI, model risk management, explainability, or bias testing in regulated industries.
• Experience in fintech, banking, lending, risk, fraud, or financial data systems.