Straive · 2 hours ago

Architect (Machine Learning)

Nashville, TN

Full-time

Remote

Senior Level

7+ years exp

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Hiring Manager

Abesha Rayan

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Responsibilities

Partner with data scientists and ML engineers throughout the entire ML lifecycle, from model development to production deployment.

Design and implement MLOps pipelines utilizing tools and frameworks such as TensorFlow Serving, Kubeflow, MLflow, or similar solutions.

Develop and implement data pipelines and engineering infrastructure to support enterprise-scale machine learning systems, including tasks like data ingestion, preprocessing, transformation, feature engineering, and model training.

Design and implement cloud-based MLOps solutions on platforms like Azure ML, Azure Databricks, AWS SageMaker, or Google Cloud AI Platform.

Demonstrated hands-on experience with Azure cloud services, including Azure ML, Azure DevOps, AKS, Azure Container Registry (ACR), Azure Application Insights, and Azure Log Analytics.

Experience with containerization technologies like Docker and Kubernetes is advantageous.

Deploy and maintain various types of machine learning models in production, particularly in text/NLP and generative AI applications.

Build CI/CD pipelines using tools such as GitLab CI, GitHub Actions, Airflow, or similar solutions to automate the ML lifecycle.

Conduct data science model reviews, focusing on code refactoring, optimization, containerization, deployment, versioning, and monitoring model quality.

Facilitate data model development with a focus on auditability, versioning, and data security, including practices like lineage tracking, model explainability, and bias detection.

Mentor junior MLOps engineers and collaborate with consulting, data engineering, and development teams.

Qualification

Find out how your skills align with this job's requirements. If anything seems off, you can easily click on the tags to select or unselect skills to reflect your actual expertise.

MLOpsGenerative AINLPCloud SolutionsData EngineeringMachine Learning LifecycleCI/CD PipelinesContainerizationAzure ExpertiseComputer VisionModel DeploymentCollaboration Skills

Required

Minimum of 7 years of work experience, with at least 3 years focused on MLOps.

Strong expertise in Generative AI, advanced NLP, computer vision, and ML techniques.

Proven experience in designing, developing, and deploying production-grade AI solutions.

Excellent communication and collaboration skills, with the ability to work independently and as part of a team.

Strong problem-solving and analytical abilities.

Stay current with the latest advancements in MLOps technologies and actively evaluate new tools and techniques to enhance the performance, maintainability, and reliability of machine learning systems.