Senior Software Engineer, Datasnipper

• 5+ years in backend or platform engineering, with strong production Python
• Experience building or running LLM inference infrastructure: a gateway or routing layer with multiple providers, failover, rate limiting and cost attribution
• Experience with cloud at the infrastructure level and infrastructure as code (we run across Azure and GCP with Terraform)
• Experience running a shared service in production: on-call, incidents, postmortems, SLOs
• Hands-on experience with observability tools (OpenTelemetry, Grafana), including instrumenting services and designing dashboards and alerts
• Comfort with privacy and compliance work: PII handling, anonymisation, retention, access control
• Experience building platform or shared-service capabilities consumed by multiple internal teams
• Ownership: You own work end-to-end, anticipate issues, and ensure high-quality delivery without close supervision
• Growth Mindset: You encourage open feedback exchange and provide clear, balanced feedback that helps others grow
• Collaboration: You build strong cross-functional relationships and influence peers through expertise, data, and empathy
• Adaptability: You navigate ambiguity calmly, model positive behavior, and help peers adjust through clear communication
• Judgment: You exercise sound judgment in ambiguous situations, balance speed and accuracy, and adjust priorities proactively

Desirable

• Own the LLM gateway: routing, provider failover, rate limits, retries, and cost attribution across multiple model providers
• Deploy, version and deprecate models across clouds, regions and environments, including quota and capacity planning, managed as infrastructure as code
• Build the shared evaluation platform: versioned datasets, experiment tracking, run and result schemas, reporting, and trace linkage back to the run
• Own the infrastructure for async and long-running AI workloads
• Own observability for AI traffic: latency, retries and fallbacks, token usage, cost and errors, per team and per use case
• Take part in the on-call rotation, run incidents, and close the follow-ups
• Implement the security and compliance controls the platform is held to: retention, access control, RBAC and SSO
• Define and maintain clean integration contracts between the platform and the teams that consume it
• Partner with product and ML engineers to turn their requirements into platform capabilities that are self-service rather than a request queue

• Recruiter screen
• Hiring Manager interview
• Peer programming session
• System design interview
• Final interviews with Engineering leadership