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.
Required Qualifications
Extensive experience in ML engineering, platform engineering, or large-scale distributed systems.
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, Terraform/Infrastructure-as-Code, etc.
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.
6+ years of progressive related experience
Skills
CI/CD, Machine Learning (ML), Machine Learning Model Development, Machine Learning Operations, Microsoft Azure, Python Automation, Python Frameworks
Compensation
Compensation offered for this role is 110,000.00 - 181,025.00 annually and is based on experience and qualifications.
The candidate(s) offered this position will be required to submit to a background investigation.
Joining our team isn’t just a job — it’s an opportunity. One that takes your skills and pushes them to the next level. One that encourages you to challenge the status quo. One where you can shape the future of protection while supporting causes that mean the most to you. Joining our team means being part of something bigger – a winning team making a meaningful impact.
Allstate generally does not sponsor individuals for employment-based visas for this position.
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Allstate provides a comprehensive technology setup, including a laptop, monitors, headset, keyboard, and mouse. Employees eligible to work from home also receive a monthly connectivity reimbursement to help offset internet costs.
When working from home, you must have a dedicated, private workspace free from distractions, along with appropriate desk and seating. Reliable internet is required, with minimum speeds of 50 MB download and 5 MB upload.
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Note: Internal candidates are only asked to join on video; a Valid Photo Identification is not needed for the interview.
