MLOps Architect

Description

Summary: Seeking a Senior MLOps Architect to lead high-stakes AI and Data projects for enterprise customers, acting as a technical authority and trusted advisor to bridge experimental data science with production operations primarily on Google Cloud Platform. Highlights: 1. Lead high-stakes AI and Data projects for enterprise customers 2. Act as a trusted advisor, owning architecture and delivery 3. Design robust, scalable MLOps architectures using Google Cloud Platform We are looking for a Senior MLOps Architect to lead high\-stakes AI and Data projects for our enterprise customers. In this role, you will act as the technical authority, helping clients bridge the gap between experimental data science and production\-grade operations primarily on Google Cloud Platform. You will lead projects that involve building end\-to\-end MLOps pipelines from scratch, migrating workloads to Vertex AI, and standardizing model deployment. You will usually act as the "trusted advisor" owning the architecture and the delivery. **Key Responsibilities** * Customer Leadership: Lead technical kickoffs, discovery workshops, and architecture reviews directly with client CTOs, VP R\&D, and Data Science leads. * Architecture \& Design: Design robust, scalable MLOps architectures using Google Cloud Platform services (Vertex AI, GKE, BigQuery, Cloud Build, Cloud Storage). * Implementation \& Automation: Build "Golden Paths" for model deployment. Implement CI/CD pipelines for ML, automated retraining workflows, and model monitoring systems to allow Data Scientists to deploy self\-sufficiently. * Production Engineering: Operationalize ML models in high\-scale environments. Troubleshoot complex infrastructure issues (e.g., GPU provisioning, container orchestration, scaling strategies). * Strategic Advisory: Advise customers on best practices for MLOps maturity, cost optimization (FinOps for AI), and data governance. Requirements (Must Have) * MLOps Experience: At least 3\+ years specialized in MLOps and building production ML pipelines. * Google Cloud Expert: Deep, hands\-on experience with GCP core services (Compute Engine, GKE, IAM, Networking) and specifically Vertex AI (Pipelines, Feature Store, Model Registry) Requirements: * Customer\-Facing Skills: Proven ability to lead projects, manage stakeholders, and explain complex technical concepts to clients. * Containerization \& Orchestration: Strong proficiency with Docker and Kubernetes (GKE). * Coding: Strong proficiency in Python and SQL. * CI/CD for ML: Experience implementing pipelines using tools like Cloud Build, GitHub Actions, or Jenkins. Big Advantage (Nice to Have) * Databricks Expertise: Experience with the Databricks Lakehouse platform, Unity Catalog, and MLflow is a major plus. Many of our clients use Databricks alongside GCP, so this skill will be highly valued. * Certifications: Google Cloud Professional Machine Learning Engineer or Professional Cloud Architect. * GenAI Experience: Experience deploying Large Language Models (LLMs) or working with Gemini/Claude APIs in production.