Job Description

Job Title: Machine Learning Workflow Automation Architect
Location: [Specify Location] Hybrid / Onsite Options
Employment Type: Full-Time

Position Summary:
We are seeking a Machine Learning Workflow Automation Architect to lead the architecture, design, and optimization of scalable ML workflows and automation pipelines in the cloud. This role bridges data science, machine learning, and cloud infrastructure, helping organizations modernize ML model deployment, optimize compute and storage, and streamline workflow automation at scale. The architect will act as a strategic advisor to both engineering and IT leadership, ensuring secure, automated, and high-performance ML workflows.

Key Responsibilities:

• Architect and implement end-to-end ML workflow automation environments on cloud platforms, including compute, storage, networking, and orchestration infrastructure for large-scale ML training, inference, and analytics.

• Lead cloud migration and modernization initiatives for on-premises ML workloads to the cloud using scalable services, orchestration frameworks, and automated pipelines.

• Design cost-efficient architectures leveraging auto-scaling, elastic compute, and dynamic storage solutions for high-performance ML workflows.

• Integrate automation and DevOps pipelines using Infrastructure-as-Code (CloudFormation, Terraform, CDK) and CI/CD tools to enable repeatable and compliant ML deployments.

• Collaborate with data science teams to optimize model training, inference, and data preprocessing pipelines for performance and scalability.

• Advise on data management strategies for large-scale ML datasets, including data lakes, object storage, lifecycle policies, and hybrid storage solutions.

• Implement and enforce security, identity, and compliance best practices in ML workflow environments.

• Provide subject matter expertise in workflow orchestration, ML job scheduling, and automation frameworks to optimize throughput and reliability.

• Mentor ML engineers, data engineers, and DevOps teams, establishing best practices and contributing to reusable architectures and playbooks.

Required Qualifications:

• 10+ years of experience in cloud infrastructure, workflow automation, or ML architecture roles.

• 5+ years of hands-on experience designing and deploying ML workflows or MLOps pipelines on cloud platforms (AWS, Azure, GCP).

• Strong expertise with ML workflow orchestration tools (e.g., Airflow, Kubeflow, Prefect) and scalable compute/storage services.

• Proficiency with cloud core services: compute, object storage, managed Kubernetes, serverless functions, monitoring, IAM, and networking.

• Solid scripting and automation experience (Python, Bash, Terraform/CDK, or similar).

• Experience with workflow job scheduling, pipeline optimization, and performance tuning.

• Proven ability to translate complex technical challenges into scalable business solutions and collaborate effectively with stakeholders and leadership.

Preferred Qualifications:

• Cloud certifications (AWS Solutions Architect Professional, Azure Architect, or equivalent).

• Experience integrating ML workflows with data analytics or AI pipelines (e.g., SageMaker, EMR, Spark, Databricks).

• Familiarity with hybrid cloud architectures and dynamic scaling strategies for ML workloads.

• Background in high-performance computing, data-intensive workloads, or technology-focused industries.

Work Environment:

• Hybrid or flexible work schedule supporting remote and onsite collaboration.

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