MLOps Architect – Google Cloud Platform (GCP)

Overview We are looking for a Principal MLOps Architect with deep expertise in Google Cloud Platform (GCP) to lead the design, migration, and operationalization of enterprise AI/ML workloads. This role is responsible for defining and implementing the end-to-end MLOps architecture — from data ingestion to model deployment and monitoring — enabling scalable, secure, and automated AI delivery on GCP. The ideal candidate brings a strong background in cloud architecture (AWS/Azure to GCP migration), MLOps strategy and tooling, and AI/ML production systems. This person will partner with data scientists, cloud engineers, and business stakeholders to modernize ML infrastructure, streamline delivery pipelines, and accelerate AI adoption across the enterprise. Responsibilities: • Architect and lead migration of AI/ML platforms and pipelines from AWS/Azure to GCP, ensuring performance, compliance, and cost efficiency. • Define and implement MLOps architecture frameworks leveraging Vertex AI, GKE, Cloud Build, BigQuery, and Dataflow for training, deployment, and continuous integration of models. • Establish standardized CI/CD pipelines for ML using GitHub Actions, Jenkins, ArgoCD, and Terraform, integrating model validation, automated testing, and deployment gating. • Design scalable data ingestion and feature engineering pipelines using BigQuery, Dataflow, Pub/Sub, and Dataproc, supporting batch and streaming ML use cases. • Develop and manage model registries, feature stores, and artifact repositories to ensure reproducibility and governance across ML lifecycles. • Integrate observability and monitoring through Vertex AI Model Monitoring, Prometheus, Grafana, and OpenTelemetry, including drift detection and performance metrics. • Enforce security and compliance best practices (IAM, KMS, VPC Service Controls, Secrets Manager) and align with Zero Trust principles. • Collaborate with AI and Data Science teams to productionize models and LLMs, optimizing for scalability, reliability, and maintainability. • Research and introduce Generative AI (LLM/RAG) patterns using Vertex AI Search, LangChain, and vector databases (FAISS, Pinecone) for enterprise adoption. • Provide technical leadership, architectural guidance, and mentorship to engineering teams in adopting cloud-native and MLOps practices. Required Skills & Experience • 10–15 years of experience in cloud architecture, data engineering, or AI/ML engineering with 5+ years on GCP. • Strong proficiency in Vertex AI, GKE, BigQuery, Dataflow, Cloud Run, and Cloud Build. • Demonstrated success in migrating data and ML workloads from AWS or Azure to GCP. • Deep understanding of MLOps frameworks (MLflow, Vertex AI Pipelines, Kubeflow, Airflow) and CI/CD automation for ML workloads. • Expertise in Python, PySpark, TensorFlow, PyTorch, and integrating ML models into production pipelines. • Solid grasp of data governance, security, and compliance frameworks (CIS, NIST, SOC2, HIPAA). • Proven experience designing scalable, containerized ML architectures using Kubernetes and microservices principles. • Strong communication and stakeholder-management skills — able to translate business objectives into actionable MLOps architecture blueprints. Preferred Qualifications • GCP Professional Machine Learning Engineer or Professional Cloud Architect certification. • Experience building LLM and RAG pipelines using Vertex AI, LangChain, and vector databases. • Familiarity with multi-cloud and hybrid AI environments leveraging Anthos. • Exposure to AI platform modernization, model observability, and cost optimization in enterprise settings.