AI/ML Engineer
Q1 Technologies
Job Description
Must Have Technical/Functional Skills
Technical Skills
Key Responsibilities
ML Model Development - Insurance Use Cases
Technical Skills
Must Have Technical/Functional Skills
Technical Skills
- 4-8+ years in ML engineering with production deployments.
- Strong Python expertise (Pandas, NumPy, Scikit-learn, PyTorch/TensorFlow).
- Hands-on with Amazon SageMaker Pipelines & Feature Store.
- Experience building containerized ML solutions (Docker).
- Knowledge of API deployment (REST/gRPC).
- SQL expertise for insurance data modeling.
- Deep experience in:
- SageMaker model training & deployment
- IAM policies & VPC configurations
- S3 lifecycle policies
- Infrastructure as Code (CloudFormation / Terraform)
Key Responsibilities
ML Model Development - Insurance Use Cases
- Build predictive models for:
- Claims severity & frequency prediction
- Fraud detection (SIU support models)
- Underwriting risk scoring
- Loss ratio forecasting
- Policy lapse prediction
- Implement NLP models for claims notes, adjuster reports, and policy documents.
- Develop computer vision models for damage assessment (auto/property insurance).
- Design end-to-end ML pipelines using:
- Amazon SageMaker (training, pipelines, endpoints, feature store)
- AWS Lambda
- Amazon S3
- Amazon ECR
- Amazon ECS / Amazon EKS
- Implement CI/CD for ML using:
- AWS CodePipeline
- AWS CodeBuild
- Manage model versioning, retraining workflows, and rollback strategies.
- Deploy real-time and batch inference endpoints.
- Implement model monitoring (data drift, concept drift, bias detection).
- Implement secure ML architecture aligned to insurance regulatory requirements.
- Encrypt data at rest and in transit.
- Document model governance and validation processes for actuarial and compliance review.
Technical Skills
- 4-8+ years in ML engineering with production deployments.
- Strong Python expertise (Pandas, NumPy, Scikit-learn, PyTorch/TensorFlow).
- Hands-on with Amazon SageMaker Pipelines & Feature Store.
- Experience building containerized ML solutions (Docker).
- Knowledge of API deployment (REST/gRPC).
- SQL expertise for insurance data modeling.
Vacancy posted 3 days ago
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