Lead Backend Engineer, ML Systems

Lead Backend Engineer, ML Systems

Location: United States

Work Arrangement: 100% Remote

Base Salary: $175,000–$200,000

Work Authorization: U.S. Citizens and Green Card holders only

About the Company

Our client is an established B2B SaaS technology company providing analytics and workflow solutions across the health and human services space. Its platform brings together large, complex datasets and turns them into analytics, insights, and decision-support capabilities for customers.

The company already has an established, customer-facing SaaS analytics platform in production and is making a significant investment in expanding its AI and machine learning capabilities. The focus is not simply on experimenting with AI, but on turning ML capabilities into scalable, reusable features within the existing product.

About the Role

We are looking for a hands-on Lead Backend Engineer, ML Systems to bridge the gap between Data Science and production software engineering.

This person will work closely with Data Scientists to take machine learning models and prototypes from proof of concept through production, designing the backend services, deployment patterns, infrastructure, monitoring, and reusable frameworks needed to operate them reliably at scale.

This is a technical leadership role, not a people-management position. You will remain hands-on while helping define architecture, engineering patterns, frameworks, and technical direction for how ML capabilities are integrated into an established SaaS platform.

The role sits within a small Platform Engineering team and partners closely with Data Science and senior technical and innovation leadership.

What You'll Do

• Partner directly with Data Scientists to move predictive and statistical ML models from prototype or POC into production.
• Design and build backend services and APIs that expose ML predictions, scoring, inference, and other model-driven capabilities to customer-facing applications.
• Own the engineering lifecycle from architecture and implementation through deployment, rollout, monitoring, reliability, and ongoing production support.
• Establish reusable frameworks, services, and engineering patterns for integrating ML capabilities across the platform.
• Make architectural decisions around model serving, infrastructure, scalability, security, maintainability, and build-versus-buy tradeoffs.
• Define monitoring and observability approaches for production ML services, including performance, reliability, and model-related production behavior.
• Integrate modern AI capabilities such as LLMs, RAG, and agentic workflows where appropriate.
• Work within and evolve an established production platform rather than building an isolated greenfield AI environment.
• Provide Lead-to-Principal-level technical direction while remaining actively involved in implementation and delivery.

What We're Looking For

• Significant software engineering experience with a strong foundation in backend services, distributed systems, and production application development.
• Lead, Staff, or Principal-level experience making architectural decisions and providing technical direction while remaining hands-on.
• Demonstrated experience partnering with Data Science teams to take traditional predictive or statistical machine learning models into production .
• Personal ownership or significant hands-on involvement in productionizing ML systems, including backend integration, deployment, rollout, monitoring, and production support.
• Experience designing scalable and reusable services rather than deploying models as isolated one-off solutions.
• Strong understanding of production engineering practices including cloud infrastructure, CI/CD, testing, observability, reliability, security, and operational support.
• Experience evaluating technical approaches and articulating architectural tradeoffs.
• Ability to work with ML systems that integrate into an existing backend/service-oriented application environment.

Nice to Have

• Experience with Java/Spring or integrating ML capabilities with Java-based backend services.
• Python and API frameworks such as FastAPI.
• Model serving, ML pipelines, MLflow, or similar ML production tooling.
• Experience with LLM APIs, RAG, vector databases, prompt engineering, or agentic workflows.
• Experience with analytics, BI, decision-support, or other data-intensive SaaS applications.
• Experience in healthcare, health technology, or human services.
• AWS, Azure, or GCP experience.

Why This Role?

This is an opportunity to have significant ownership over how machine learning becomes production software , rather than focusing solely on model development or AI experimentation.

You will help establish the architecture, frameworks, and engineering practices used to move Data Science work into a mature customer-facing platform. The role combines hands-on backend engineering with ML productionization and Lead-to-Principal-level technical influence.

You will also have exposure to both established predictive ML and newer technologies such as LLMs and RAG, while helping determine where each approach makes sense within a real production product.

Location & Schedule

This is a 100% remote position within the United States . The role is full-time and does not require regular onsite attendance.