Job Description

• Implement and operate CI/CD pipelines, automated testing and release processes for AI/ML workloads.
• Build and maintain model registry, model serving and AI gateway integrations for LLM APIs and internal applications.
• Configure and maintain observability for model usage, cost, token consumption, latency, reliability and quality signals using tools such as Prometheus, Grafana, logging and alerting platforms.
• Support the transition of workloads from sandbox or PoC environments into production by following defined standards, runbooks and support models.
• Implement reusable technical components for LLM API integration, RAG pipelines, evaluation pipelines and integration with business applications.
• Execute infrastructure-as-code for platform environments across container and cloud infrastructure, including Docker, Kubernetes and Helm-based deployment patterns.
• Maintain runbooks, operating procedures, technical documentation and operational dashboards for platform components.
• Support incident analysis, reliability improvements, cost optimization and lifecycle maintenance for production AI workloads.
• Work with nearshore, system integration or cloud partners on specific implementation tasks as directed by the AI Platform Engineer.
• Collaborate with data engineering, application development, cloud platform and security teams on integration, identity, access and deployment requirements.

Qualifications

• 3-5 years of experience in DevOps, cloud engineering, ML engineering, MLOps or platform engineering.
• Hands-on experience with CI/CD, infrastructure as code and automated deployment in production environments.
• Strong practical Python skills and Git-based development workflows.
• Experience with Docker and Kubernetes; deployment tooling such as Helm is desirable.
• Working experience with Azure or AWS cloud services, including compute, storage and IAM concepts.
• Experience with observability tooling such as Prometheus, Grafana, logging platforms and alerting practices.
• Familiarity with MLOps concepts such as model registries, evaluation pipelines, drift monitoring and model lifecycle management.
• Understanding of network isolation, identity, secrets management and API access control.
• Fluency in English, spoken and written.