Machine Learning Ops Engineer

• Architect and orchestrate a seamless multi-cloud environment. Manage the AI tech stack and systems alongside the enterprise data infrastructure using Terraform
• Design and maintain robust DataOps pipelines implementing Medallion Architecture (Bronze / Silver / Gold). Use Airflow to orchestrate DAGs and ensure data quality / lineage before it reaches the models
• Ensure excellence in the MLOps lifecycle by implementing the "4 C's": CI (Automated linting/testing in GitLab), CD (Safe rollout strategies), CT (Automated retraining triggers), and CM (Continuous Monitoring of drift / latency)
• Champion Finance operations (cost and efficiency) for ML and LLM systems. Implementing approaches to prevent redundant API calls and scripting automated "Kill Switches" for runaway GPU instances or token spikes
• Secure the platform by architecting services to allow our team to access different resources securely from different environments, managing IAM Identity Center for least-privilege access
• Participate in the evaluation of observability tools to trace token usage, error rates per users and other other measures

Professional Experience and Qualifications

• 5+ years of technical experience, with a proven track record of shipping ML pipelines in production
• Multi-Cloud Fluency: Deep expertise in architecting solutions on major cloud platforms (e.g. AWS, GCP). Strong operational grasp of cloud services (e.g. Security, Networking, Storage, AI)
• Experience in LLM Observability & Cost Optimisation: Experience setting up stacks with self-hosted tools (e.g. Langfuse, LangSmith, Phoenix). Ability to implement caching strategies (e.g. Redis / Memcached)
• Certifications: Google Professional Machine Learning Engineer or AWS Certified Machine Learning - Specialty / DevOps Engineer - Professional certification
• Holding a Bachelor’s degree in Computer Science, Engineering, or related fields
• Expert in Infrastructure as Code (IaC): Mastery of IaC (e.g. Terraform, OpenTofu). Experience writing modular, reusable code for multi-environment setups (Dev / Staging / Prod)
• Proficient in DataOps: Proven implementation of Medallion Architecture on a Data Lakehouse. Proficiency with Apache Airflow (writing custom operators), with data quality tools like dbt tests, and with data governance tools (e.g. OpenMetadata)
• Mastery of CI/CD & Automation: Advanced configuration of GitLab CI (e.g. Runners, Secrets Management). Experience with CML (Continuous Machine Learning) is a plus
• Proficient in Containerisation: Mastery of Docker, Kubernetes and orchestration (e.g. VM, K8s)
• Passionate about cost management and efficiency: You view efficiency as a dual mandate, optimising financial costs while maximising system performance

Sound like you? Apply now!