AI DevOps Engineer (MLOps & Cloud)
Summary
Build and maintain cloud infrastructure and CI/CD pipelines for AI/ML systems, automate deployments, and ensure scalable, secure, and observable production environments.
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 containerization 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)