Senior GCP Cloud MLOPS Engineer

Overview

Machine Learning Engineer

Key Responsibilities

• You’ll take ML models from experimentation to production by designing and implementing end-to-end MLOps pipelines on Google Cloud Platform (GCP), including:
• Data ingestion, feature engineering, model training, evaluation, and deployment
• Building robust data pipelines and feature stores using BigQuery and Dataflow / Apache Beam
• Training, versioning, and managing models via Vertex AI Workbench and Model Registry
• Deploying real-time inference systems using Vertex AI Endpoints
• Containerizing services with Docker and deploying via Cloud Run or GKE
• Orchestrating workflows using Cloud Composer (Apache Airflow)
• Developing secure REST APIs (FastAPI/Flask) for seamless integration with core systems
• Implementing model monitoring to detect drift and ensure long-term performance

🌐 Architecture & Innovation

• Design hybrid cloud AI solutions integrating on-prem systems with GCP
• Build secure pipelines for data anonymization and tokenization (PHI/PII protection)
• Ensure compliance with regional healthcare regulations and data sovereignty standards
• Collaborate with cross-functional teams to translate business and clinical needs into scalable AI solutions
• ✅ Job Requirements:
• 3–5+ years of experience deploying ML models into production environments
• Strong expertise in GCP for ML workloads
• Proficiency in Python and ML frameworks (TensorFlow, PyTorch, scikit-learn)
• Experience with FastAPI/Flask for model serving
• Solid DevOps skills: Docker, CI/CD (Cloud Build, GitHub Actions), Terraform
• Strong understanding of ML evaluation metrics (Precision, Recall, ROC-AUC)
• Bachelor’s or Master’s degree in Computer Science, AI, or a related field