Senior Machine Learning Site Reliability Engineer

What you’ll do:

Hands-on Reliability & System Engineering: Design, build, and operate reliable and scalable systems by defining and monitoring SLOs/SLIs, working directly on production infrastructure, and collaborating closely with software engineers on system design and reliability improvements

Automation, Operations & Incident Response: Actively develop automation for infrastructure and operational workflows to eliminate toil and reduce MTTR, participate in and lead incident response, and drive blameless post-incident reviews with concrete follow-ups implemented in code and tooling

Performance, Capacity & Security: Continuously analyze and optimize system performance and cost, provide data, insights, and recommendations to inform capacity planning, and support security best practices through hands-on vulnerability remediation and threat mitigation

What we’re looking for:

SRE & Cloud Engineering: Hands-on experience with SRE practices in production, strong AWS expertise, Kubernetes, networking, DNS, and Infrastructure as Code (Pulumi preferred, Terraform a plus)

Automation, Software Engineering and MLOps: Demonstrate strong software engineering fundamentals with an emphasis on code quality and maintainability. This includes solid Python proficiency and deep knowledge of the Python ecosystem (testing, debugging, packaging), hands-on experience with PySpark, and a consistent focus on writing clean, well-structured, and maintainable code. Familiarity with MLOps practices such as model registries, model versioning, retraining workflows, and end-to-end deployment lifecycles is also expected

Reliability, Data & Operations: Add stakeholder engagement and mentoring e.g. lead incident response and RCAs, improve system reliability, and engage stakeholders to propose solutions, share learnings, and mentor others

Nice to have:

Regulated Environments & Security: Experience operating in highly regulated industries (e.g. Insurance, Banking, Healthcare), managing sensitive data, and supporting secure networking setups, including exposure to security technologies such as Cloudflare

Distributed Systems & Microservices: Strong understanding of microservices architectures, their principles and trade-offs, with the ability to troubleshoot and maintain distributed systems and supporting technologies (RabbitMQ, Kafka, PostgreSQL, Redis)

Observability & Platform Operations: Hands-on experience with Datadog for platform and application monitoring, performance optimisation, and solid fundamentals in database structures and operational troubleshooting, with exposure to systems built in languages such as Rust and Elixir