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Company Description
Blend is a premier AI services provider, committed to co-creating meaningful impact for its clients through the power of data science, AI, technology, and people. With a mission to fuel bold visions, Blend tackles significant challenges by seamlessly aligning human expertise with artificial intelligence. The company is dedicated to unlocking value and fostering innovation for its clients by harnessing world-class people and data-driven strategy. We believe that the power of people and AI can have a meaningful impact on your world, creating more fulfilling work and projects for our people and clients. For more information, visit www.blend360.com
Job Description
We are seeking a highly skilled Lead MLOps Engineer to join our team and drive the end-to-end deployment, scalability, and operationalization of machine learning models in production. You will collaborate closely with data scientists, data engineers, and DevOps teams to ensure seamless CI/CD, reproducibility, monitoring, and governance of ML pipelines
Key Responsibilities
- Lead the design and implementation of scalable MLOps solutions for deploying machine learning models to production.
- Build and maintain CI/CD pipelines for ML workflows using tools such as MLflow, Azure DevOps, and Terraform.
- Develop and manage infrastructure on Microsoft Azure, including Azure ML, Azure Kubernetes Service (AKS), and other PaaS offerings.
- Manage and automate infrastructure provisioning using Terraform.
- Implement model tracking, versioning, and deployment strategies using MLflow and best MLOps practices.
- Enable scalable and efficient model serving, monitoring, and retraining pipelines.
- Collaborate with DevOps and Data Engineering teams to integrate ML systems into the larger data ecosystem.
- Leverage Databricks for collaborative development, feature engineering, and model deployment.
- Establish robust monitoring, logging, and alerting for deployed models to ensure reliability and performance.
- Mentor junior team members and provide technical leadership on MLOps standards and practices.
Qualifications
- 6+ years of experience in DevOps or MLOps with a strong focus on production-grade ML solutions.
- Expertise in Terraform for IaC (Infrastructure as Code).
- Hands-on experience with Azure cloud services, especially Azure ML and AKS.
- Proficient in MLflow for model tracking, versioning, and lifecycle management.
- Experience with Databricks for collaborative ML and data engineering workflows.
- Strong understanding of containerization and orchestration using Docker and Kubernetes.
- Familiarity with monitoring tools and techniques to manage ML systems in production.
- Good knowledge of Python and common ML libraries.
- Strong problem-solving skills and the ability to work independently and within a team.
Additional Information
Preferred Qualifications
- Azure Certifications (e.g., Azure DevOps Engineer, Azure Solutions Architect).
- Experience with other MLOps tools like Kubeflow, TFX, or Seldon.
- Exposure to data versioning tools like DVC.
