Key Responsibilities

• Design, implement, and maintain scalable, secure cloud infrastructure for AI/ML solutions

• Build and manage Infrastructure as Code (IaC) using tools such as Terraform or CloudFormation

• Develop and maintain CI/CD pipelines for AI applications and model deployment

• Support end-to-end MLOps lifecycle (training, versioning, deployment, monitoring)

• Automate deployments using best practices (blue/green, canary releases, rollback strategies)

• Configure monitoring, alerting, and observability (logs, metrics, tracing) for production systems

• Optimize performance, scalability, and cost (compute, storage, GPU usage)

• Use AI tools (e.g. Copilot, ChatGPT, Claude, Cursor) to accelerate scripting, automation, and incident resolution

• Troubleshoot production issues and ensure high system reliability and availability

• Support developers and data scientists on DevOps tooling and best practices

• Implement DevSecOps standards, including security, secrets management, and compliance

• Maintain clear technical documentation for infrastructure and processes

Skills & Experience

• 5+ years of experience in DevOps, SRE, or platform engineering

• Strong experience in MLOps and deployment of AI/ML solutions in production

• Proficiency with cloud platforms (AWS, Azure, or GCP) and related AI services

• Expertise in Infrastructure as Code (Terraform, CloudFormation, ARM templates)

• Hands-on experience with containerisation and orchestration (Docker, Kubernetes, Helm)

• Strong CI/CD experience (GitHub Actions, GitLab CI, Jenkins, Azure DevOps)

• Familiarity with MLOps tools (MLflow, Kubeflow, Weights & Biases, SageMaker Pipelines)

• Proficiency in scripting and automation (Python, Bash)

• Experience with monitoring and observability tools (Prometheus, Grafana, ELK, Datadog)

• Knowledge of DevSecOps practices and security standards

• Experience with secrets and configuration management (Vault, AWS Secrets Manager)

Preferred Skills & Attributes

• Experience optimising AI workloads using GPUs

• Ability to diagnose and resolve issues using AI-assisted tools

• Strong problem-solving and analytical thinking

• Pragmatic approach balancing automation, speed, and reliability

• Attention to detail in infrastructure design and security

• Proactive, with strong ownership and initiative

• Strong collaboration and support mindset

• Comfortable working in fast-paced, agile environments

Work Environment

• Agile, fast-paced innovation environment

• Close collaboration with AI engineers, data scientists, and infrastructure teams

• Strong focus on automation, scalability, and production-ready AI systems

• English-speaking environment (fluency required)