Why This Role Stands Out
This hybrid role offers a fantastic opportunity to architect and build cutting-edge AI and data platforms that will revolutionize the insurance industry, fostering significant career growth and skill development. You'll thrive here if you are a collaborative engineer passionate about MLOps, cloud infrastructure, and driving innovation in a supportive, learning-focused environment. Apply now to shape the future of AI at a reputable company!
Quick Overview
Job Description
Allstate is seeking a Senior AI Cloud Platform Engineer to design, build, and optimize secure, scalable AI and data platforms supporting next-generation insurance and financial solutions. You will architect and implement cloud-native machine learning infrastructure, MLOps pipelines, and APIs for advanced analytics and generative AI use cases across underwriting, claims, and customer experience. Partnering with data scientists, security, and product teams, you'll ensure reliable, compliant deployment of AI workloads, drive automation and observability, and mentor engineers in a collaborative, learning-focused culture.
Responsibilities
- Design and architect secure, scalable AI and data platforms in the cloud
- Build and maintain MLOps pipelines for model training, deployment, and monitoring
- Collaborate with data science, product, and security teams on AI solutions for insurance use cases
- Implement APIs and services to operationalize machine learning and generative AIEnsure compliance, governance, and observability of AI workloads in production
- Optimize cloud performance, reliability, and cost for AI/ML infrastructure
- Automate infrastructure provisioning using infrastructure-as-code
- Mentor engineers and promote best practices in AI platform engineering
Required Skills
- Cloud platforms (AWS, Azure, or GCP)
- Kubernetes and containerization (Docker)
- Infrastructure as Code (Terraform, Cloud
- Formation, or similar)
- Python or similar language for ML and services
- MLOps tools (MLflow, Kubeflow, Sage
- Maker, Vertex AI, or Databricks)
- CI/CD pipelines (Git
- Hub Actions, Jenkins, Azure Dev
- Ops, etc.)
- Data engineering (Spark, Kafka, or similar)
- Monitoring and observability (Prometheus, Grafana, Cloud
- Watch, etc.)
- Security and compliance in cloud environments
- REST/g
- RPC API design and implementation
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