Senior ML Software Engineer (MLOps)

Location: Hybrid in London, United Kingdom

Company

hirify.global develops payment products and ML-backed services for customer-facing financial solutions.

What you will do

• Design, implement, and deploy backend services, APIs, and software features that operationalize ML models in production.
• Own the technical direction, reliability, performance, and ongoing operations of production ML services.
• Contribute to shared ML platform tooling and engineering standards that improve delivery and developer velocity.
• Maintain CI/CD pipelines, testing frameworks, monitoring, and logging; coordinate releases and rollouts.
• Participate in on-call rotations, lead incident response, perform root cause analysis, and deliver remediation.
• Provide technical guidance to engineers and data scientists, mentor teammates, and drive continuous improvement.

Requirements

• 5+ years of experience as a software engineer, MLOps engineer, or in a similar role operating backend services or ML-backed APIs.
• Production experience deploying ML models as services, designing APIs, and integrating services.
• Strong Python and backend engineering skills; experience with FastAPI/OpenAPI, Flask, Go, or Java is relevant.
• Production experience with Docker, Kubernetes or similar orchestration, and infrastructure as code such as Terraform.
• Experience owning CI/CD, monitoring and alerting, on-call operations, incident response, and production performance optimization.
• Strong communication, cross-functional collaboration, independent problem-solving, and delivery skills.

Nice to have

• Experience with MLFlow, model versioning, SageMaker Pipelines, or Databricks.
• Familiarity with real-time, high-volume data products and low-latency data stores such as DynamoDB.
• Experience with automated model and service testing, regulated industries, or mature engineering organizations.
• Familiarity with LLM tooling or retrieval-augmented generation.

Culture & Benefits

• Own high-profile ML-backed services that support customer-facing products and measurable business outcomes.
• Work with a cross-functional team of ML research engineers, product managers, and platform engineers.
• Receive autonomy to shape technical solutions, engineering standards, and product-area priorities.
• Join an inclusive, global, and collaborative engineering environment.