Software Engineer, ML Ops and Platform

Job summary

The role involves owning the ML lifecycle from data to device, focusing on designing and operating production-grade pipelines for machine learning models. Key responsibilities include maintaining CI/CD workflows, developing secure APIs, and implementing real-time model monitoring, utilizing technologies such as Docker, Kubernetes, and Python or Go for backend engineering.

Qualifications

• 5+ years building ML infrastructure, data platforms, or production ML systems at scale.
• Track record of delivering platforms and CI/CD pipelines used daily by ML or data teams.
• Hands-on experience running large-scale training on multi-tenant GPU clusters to maximize throughput and reliability.
• Built versioned dataset & lineage systems with slice-level provenance and governed access.
• Deep understanding of containerisation (Docker) and orchestration (Kubernetes/EKS) plus Infrastructure-as-Code (Terraform/CDK/Cloudformation).
• Strong backend engineering skills in Python and/or Go.
• Deep understanding of modern CI/CD, model packaging, and observability practices.
• Comfortable operating production systems, defining SLAs, and handling rollout or incident workflows.
• Comfortable using modern Agentic AI development.

Responsibilities

• Design, build, and operate reliable, observable infrastructure for training, evaluation, telemetry ingestion, and deployment.
• Maintain CI/CD workflows and automated pipelines.
• Edge-aware rollout services with staged deployment, A/B experimentation and instant rollback across Orbs, Orb Mini and Mobile Apps.
• Develop secure APIs and backend services that expose governed datasets and model artefacts at scale.
• Implement automated checks, drift detection, and alerting for real-time model monitoring.
• Champion best practices in data lineage, reproducibility, privacy-by-design, security and secure edge delivery.
• Collaborate across ML research, product, and firmware teams to streamline delivery and feedback loops.

Skills

Relocation