Build and operate production ML services for payment optimization, with end-to-end ownership across backend, APIs, reliability, and delivery.

Design, implement, and deploy software features, backend services, and APIs that operationalize ML models in production
Provide full technical ownership for existing and new production ML services in your product area, aligning engineering investments with business goals and best practices
Steward service reliability and performance using SLO-driven practices
Evolve shared platform tooling and standards to improve developer velocity, reliability, and repeatable delivery across teams
Maintain and enhance automated CI/CD pipelines, testing frameworks, and monitoring/logging
Own pre-release testing, rollouts, and release coordination for your services
Participate in on-call rotations and lead incident response, root cause analysis, and remediation for your product area
Mentor teammates and provide technical guidance to engineers and data scientists; drive continuous improvement and knowledge sharing

5+ years as a software engineer, MLOps Engineer, or similar role, with hands-on production experience operating backend services or ML-backed APIs
Proven ability to deploy ML models as production services, including API design and integration with services
Strong Python skills and service/backend engineering experience (ideally FastAPI/OpenAPI, Flask, Go, or Java)
Production experience with containerization and orchestration (ideally Kubernetes such as EKS or OpenShift), including Docker
Experience with IaC (ideally Terraform or Terraform Cloud)
Ownership experience across CI/CD pipelines, monitoring/alerting, and on-call/incident response
Ability to monitor, troubleshoot, and optimize production ML systems, including latency, throughput, and availability
Excellent communication and cross-functional collaboration skills
Ability to work independently in ambiguous environments, resolve blockers, and deliver with urgency

Experience with MLFlow/model versioning, SageMaker Pipelines, or Databricks
Familiarity with real-time, high-volume data products and low-latency data stores (e.g., DynamoDB)
Automated testing and validation frameworks for models and services
Experience in regulated industries (e.g., PCI, HIPAA, SOC2), large companies, or mature engineering teams
Optional familiarity with LLM tooling / RAG

Senior ML Software Engineer

Job Description