Senior ML Software Engineer
Total System Services, LLC (TSYS)1 day ago
London, United KingdomSenior
Responsibilities
- Design, implement, deploy, and operate backend services, APIs, and software features that operationalize ML models in production.
- Own the technical direction, reliability, performance, and ongoing operations of existing and new production ML services.
- Evolve shared ML platform tooling and standards to improve developer velocity, reliability, and repeatable delivery.
- Maintain CI/CD pipelines, testing frameworks, monitoring and logging, pre-release testing, rollouts, and release coordination.
- Participate in on-call rotations and lead incident response, root cause analysis, and remediation.
- Provide technical guidance and mentorship to engineers and data scientists while driving continuous improvement and knowledge sharing.
Requirements
- 5+ years of experience as a software engineer, MLOps engineer, or similar, with hands-on production experience operating backend services or ML-backed APIs.
- Expertise deploying ML models as production services, designing APIs, and integrating services.
- Strong Python skills and backend engineering experience, ideally with FastAPI, OpenAPI, Flask, Go, or Java.
- Production experience with containerization and orchestration, ideally Kubernetes, EKS, or OpenShift, plus Docker and infrastructure as code such as Terraform or Terraform Cloud.
- Experience owning CI/CD pipelines, monitoring and alerting, on-call responsibilities, and incident response.
- Experience monitoring, troubleshooting, and optimizing production ML systems for latency, throughput, and availability.
- Experience with MLFlow, model versioning, SageMaker Pipelines, Databricks, real-time high-volume data products, low-latency data stores such as DynamoDB, or automated model and service testing frameworks is a bonus.
- Experience in regulated industries, large companies, or mature engineering teams is a bonus; familiarity with LLM tooling or RAG is optional.
- Strong communication, collaboration, independent problem-solving, and delivery skills.
Benefits
- Opportunity to own high-profile ML-backed services powering customer-facing products and measurable business outcomes.
- End-to-end technical ownership and autonomy in a fast-paced, supportive environment.
- Collaboration with cross-functional engineering, product, ML research, and platform teams in an inclusive global organization.