MLOps Architect

Description

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.