Lead Consultant- Machine Learning Platform

Overview

Lead the architecture, build and scaling of Allstate's core machine learning platforms, providing deep technical leadership across ML infrastructure, MLOps automation, model deployment systems and cloud-native engineering.

What you'll do

• Serve as the technical lead for ML platform architecture, guiding system design, scalability, performance and reliability across platform components.
• Architect and build core ML platform services, including training and compute infrastructure, feature stores, model registries, inference runtimes and data pipelines.
• Drive architectural decisions for distributed systems, cloud-native frameworks and automated MLOps workflows that support enterprise-scale machine learning.
• Evaluate and integrate emerging ML platform technologies, tools and best practices to continuously strengthen platform capabilities.
• Design and implement robust MLOps pipelines for experiment tracking, data and model versioning, CI/CD for ML, automated retraining and model governance.
• Develop automated workflows that ensure reproducible model training, validation, deployment and lifecycle management across multiple environments.
• Implement monitoring and observability systems for model performance, data quality, drift detection and inference reliability.
• Build and optimize cloud-based ML infrastructure on Azure, AWS or GCP using Kubernetes, containerization and infrastructure-as-code.
• Develop scalable batch and streaming data pipelines using modern data engineering tools and frameworks.
• Embed security, compliance, responsible AI principles and cost optimization best practices within ML platform architecture and operations.
• Collaborate with data scientists to translate modeling needs into scalable, reusable and self-service platform capabilities.
• Work closely with security, compliance and governance teams to ensure safe and compliant deployment of AI/ML solutions.
• Partner with application engineering teams to accelerate adoption of ML services and enable consistent, high-quality production deployments.
• Provide technical mentorship, set engineering standards and contribute to documentation, best practices and ongoing platform improvements.

What you'll need

• Extensive experience in ML engineering, platform engineering or large-scale distributed systems.
• 6+ years of progressive related experience.
• Deep hands-on expertise with MLOps tools, ML frameworks, model deployment techniques and ML lifecycle automation.
• Strong proficiency in Python and backend development for machine learning systems.
• Experience with cloud platforms and ML services, including Azure ML Studio, AWS SageMaker and/or Google Vertex AI.
• Exposure to cloud storage/data such as Azure Fabric/OneLake, AWS S3 and Google Cloud Storage (GCS).
• Experience with cloud-native scanning and security tools such as Azure Defender, Microsoft Purview, AWS Security Hub, Amazon Inspector, GCP Security Command Center or equivalent services.
• Strong understanding of technologies such as Kubernetes, Docker, CI/CD and Terraform/Infrastructure-as-Code.
• Solid knowledge of system design, APIs, data pipelines and scalable ML infrastructure patterns.
• Proven ability to lead technical initiatives and influence cross-team engineering decisions.

Details

• Location: USA, Illinois (Remote).
• Compensation: $110,000.00 - $181,025.00 annually, based on experience and qualifications.
• Candidates offered this position will be required to submit to a background investigation.
• Allstate generally does not sponsor individuals for employment-based visas for this position.