Data Science Practitioner

Overview

Our client is building a new Azure MLOps platform to host recommendation models and are looking for a Azure MLOps Architect to help design and implement this platform. This is a high-impact role where you will work closely with data scientists, engineers, and AI experts to ensure the scalable, high-performance deployment of machine learning models.

As part of our team, you will oversee the architecture of MLOps workflows, model hosting, and deployment pipelines on Azure. You will also leverage your expertise in Azure-based technologies, high-performance model serving, and infrastructure automation to drive the creation of efficient and effective ML platforms.

Key Responsibilities:

• Design and architect Azure MLOps workflows, including ML model deployment pipelines and model hosting platforms for scalable, high-performance recommendation models.
• Build robust ML serving infrastructure that supports different model types, from classical machine learning to deep learning models.
• Lead efforts in MLOps engineering to ensure proper experimentation, continuous integration, and continuous deployment (CI/CD) for machine learning models in Azure.
• Work with stakeholders to develop best practices for model lifecycle management and deployment in production.
• Collaborate with data scientists to optimize models for performance, ensuring efficient resource usage in production environments.
• Implement solutions that leverage high-performance model serving technologies to meet the demands of a global user base.
• Travel to San Francisco (up to 4 weeks) for project-related collaboration and on-site work.

Required Qualifications:

• Extensive expertise in Azure Machine Learning (ML) Services and MLOps practices.
• Experience designing and implementing ML workflows, deployment pipelines, and model serving solutions in Azure.
• Solid experience in ML model experimentation and managing the full model lifecycle (development, testing, deployment).
• Proven ability to build and optimize ML deployment pipelines in Azure, using tools such as Azure DevOps and Azure Machine Learning services.
• Strong understanding of high-performance model serving, ensuring optimal scalability and availability.
• Familiarity with modern cloud infrastructure and automation (Azure DevOps, Kubernetes, AKS).
• Strong communication skills and experience working with cross-functional teams, including data scientists, engineers, and business stakeholders.

Technical Skills Qualifications:

• Experience with Databricks for distributed computing and machine learning workflows.
• Experience with Google Cloud Platform (GCP), particularly in the context of machine learning and data engineering.
• Hands-on experience with Cosmos DB, specifically DogDB/Stardog schema, and expertise in vector search and embeddings.
• Experience in serverless compute models and containers (Kubernetes, AKS).
• Knowledge of infrastructure as code tools like Terraform, with a strong focus on app development and infrastructure engineering.
• Expertise in building low-latency services, API gateways, and event hubs, with a focus on performance tuning and load management.