Why This Role Stands Out
This role offers a fantastic opportunity to drive impactful AI/ML solutions within a reputable company, fostering significant career growth and skill development in cutting-edge technologies. You'll thrive here if you're a collaborative engineer eager to mentor others and build scalable, reliable AI systems in a hybrid environment. Apply to contribute to innovative projects and make a tangible difference in customer experiences and business operations.
Quick Overview
Job Description
Allstate is seeking a Senior AI Engineer to join our Data Science & Artificial Intelligence (AI) team in the Insurance industry. You will design, build, and deploy scalable AI/ML solutions that improve underwriting, claims, fraud detection, and customer experiences. Responsibilities include developing production-grade models, collaborating with data scientists and engineers, integrating models into cloud-based systems, and ensuring reliability, fairness, and compliance. You will mentor junior teammates, drive MLOps best practices, and partner with business stakeholders to translate complex data into measurable business impact.
Responsibilities
- Design, build, and deploy scalable AI/ML solutions for underwriting, claims, and fraud detection
- Collaborate with data scientists and engineers to productionize and optimize models
- Implement MLOps pipelines, CI/CD, monitoring, and model governance
- Integrate AI services and APIs into cloud-native applications and data platforms
- Ensure AI systems meet security, reliability, fairness, and regulatory requirements
- Work with stakeholders to translate business needs into technical AI solutions
- Optimize model performance, latency, and cost in cloud environments
- Mentor junior engineers and contribute to engineering best practices and standards
- Document architectures, models, and processes for maintainability and reuse
- Evaluate and introduce new AI tools, frameworks, and practices to drive innovation
Required Skills
- Python
- Machine learning algorithms
- Deep learning frameworks (Tensor
- Flow/Py
- Torch)
- MLOps and ML pipelines (CI/CD)
- Cloud platforms (AWS/Azure/GCP)
- Data engineering (SQL, data pipelines)
- Model monitoring and observability
- API development and microservices
- Version control (Git)
- Software engineering best practices
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