MLOps Engineer

Key Qualifications

• 5+ years of software, cloud, DevOps, ML engineering, or related engineering experience, with meaningful hands-on experience supporting ML/AI workloads in production.

• Demonstrable hands-on experience with the Google Cloud AI/ML stack, particularly production deployment and operationalization of ML workloads.

• Strong Python development and automation skills.

• Practical experience building and maintaining CI/CD pipelines for ML or AI applications.

• Hands-on knowledge of containerization and orchestration using Docker and Kubernetes/GKE.

• Experience with model deployment, versioning, monitoring, rollback, and lifecycle management.

• Good knowledge of Vertex AI, or comparable managed ML platforms.

• Understanding of machine learning workflows covering data preparation, training, evaluation, deployment, monitoring, and retraining.

• Familiarity with Generative AI concepts such as LLMs, embeddings, RAG, vector databases, prompt-based applications, and agentic AI.

• Understanding of production monitoring concepts including service health, model performance, drift, latency, resource utilization, and failure monitoring.

• Familiarity with Infrastructure as Code, preferably Terraform.

• Experience troubleshooting distributed cloud applications and production ML pipelines.

• Comfortable working in an agile sprint environment with defined engineering tickets, code reviews, and release cycles.

• Basic understanding of responsible AI, security, and data privacy, with the ability to identify and escalate potential concerns.