ML Ops Engineer

In this role you will get to: AI/ML Deployment & GitOps Automation Contribute to GitOps deployment workflows with Argo that ensure AI/ML services are correctly onboarded and configured for GitOps delivery across GKE, Dataproc, Vertex AI, Dataflow, and other platform-managed compute environments. Package AI/ML services into Docker images and manage lifecycle/versioning in Google Artifact Registry. Implement CI/CD pipelines using GitHub Actions for builds, tests, image scans, and deployments. Infra as Code & Cloud Platform Engineering Develop and manage Terraform modules for Data & AI/ML platform resources such as Composer, Vertex AI, Dataproc, BigQuery, GCS, and other Data/AI/ML services. Ensure infra repeatability, reliability, and alignment with platform and infosec standards. ML Pipeline Orchestration & Model Lifecycle Build, maintain, and troubleshoot AI/ML pipelines, batch jobs, and custom training workflows through Vertex AI. Use Cloud Composer (Airflow) to orchestrate multi-stage ML workflows spanning data prep, training, evaluation, and deployment. Integrate LLMOps patterns through Gemini Enterprise, LiteLLM or similar model gateways. Kubernetes & Istio-Based Service Operation s Operate AI/ML powered microservices on GKE involving Istio gateways and service mesh patterns. Collaborate with platform teams on security tooling such as NexusIQ, StackRox, and Wiz. Monitoring, Observability & Model Quality Use Arize (or similar tools) for model drift/quality monitoring, embeddings monitoring, and LLM evaluation patterns. Implement logging, alerting, and SLOs for ML workloads and pipelines with Splunk, New Relic, Pagerduty etc.. Assist with incident response, root-cause analysis, and long-term platform improvements. DevOps Support for Internal Web Framework Provide operational guidance and deployment automation for internal Python-based frameworks used in AI/ML services. Improve developer productivity through standardized templates, CI/CD patterns, and tool Who you are: Bachelor s Degree in Computer Science or relevant experience. 4-6 years of experience in DevOps, Cloud Engineering, or MLOps roles. Strong experience with GCP (Vertex AI, GKE, Dataflow, Dataproc, Composer, BigQuery, etc.) or other major cloud providers (Azure/AWS). Hands-on expertise with Kubernetes, Vertex AI, Docker and image-based deployment workflows. High proficiency with Python or similar scripting language, especially for automating ML/infra tasks. Strong knowledge of Terraform and IaC patterns at scale. Experience deploying apps via GitOps using Argo. Proven ability to support AI/ML models in production: monitoring, pipelines, debugging, retraining loops. Illustrated history of living the values necessary to Priceline: Customer, Innovation, Team, Accountability and Trust. The Right Results, the Right Way is not just a motto at Priceline; it s a way of life. Unquestionable integrity and ethics is essential. Nice-to-Haves Experience with LLMOps toolchains (RAG pipelines, vector stores, prompt/version management, agent frameworks). Good understanding of infosec and RBAC best practices, and security posture management. Exposure to SRE best practices and error budgets for ML systems .