AI/ML Engineer
Role summary
We are seeking a skilled AI/ML Engineer to design, build, and deploy scalable machine learning solutions, focusing on the AWS ML ecosystem, MLOps, and production-grade model deployment. The role involves creating end-to-end ML pipelines, managing workflows with Amazon SageMaker, implementing MLOps best practices including CI/CD for ML, and deploying models as scalable inference endpoints. Proficiency in Docker, Kubernetes (EKS preferred), and Python with ML libraries is essential. Experience in the Insurance domain (Claims, Underwriting, Fraud Detection) is preferred. This is a full-time, onsite position.
Lebanon, New Jersey 08833 Posted March 24th, 2026
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Job Type: Full Time
Job Category: IT
Job Description
Role- AI/ML Engineer
Location-Lebanon, NJ – 08833/ NY, NY – 10010 (Onsite)
Full Time Employment
Role Overview
We are looking for a skilled AI/ML Engineer to design, build, and deploy scalable machine learning solutions. The ideal candidate will have strong experience in AWS ML ecosystem, MLOps, and production-grade model deployment, along with domain exposure in Insurance (Claims, Underwriting, Fraud Detection).
Key Responsibilities
Design and develop end-to-end machine learning pipelines for training, validation, and deployment
Build and manage ML workflows using Amazon SageMaker and AWS ML services
Implement MLOps best practices, including CI/CD pipelines for ML models
Deploy models as scalable inference endpoints and monitor performance in production
Containerize ML applications using Docker and orchestrate using Kubernetes (EKS preferred)
Collaborate with data scientists, data engineers, and business teams to translate requirements into ML solutions
Optimize models for performance, scalability, and cost-efficiency
Ensure proper versioning, monitoring, and governance of ML models
Required Skills & Qualifications
Strong experience with Amazon SageMaker and AWS ML services
Hands-on expertise in MLOps, including CI/CD for ML workflows
Experience building ML pipelines (training, validation, deployment)
Proficiency in model deployment and managing real-time/batch inference endpoints
Solid experience with Docker and containerization
Hands-on experience with Kubernetes and Amazon EKS
Programming experience in Python and ML libraries (e.g., TensorFlow, PyTorch, Scikit-learn)
Experience with version control systems (Git) and automation tools
Understanding of data engineering concepts and cloud architecture
Preferred Qualifications
Experience in the Insurance domain, including:
Claims processing automation
Underwriting risk models
Fraud detection systems
Familiarity with streaming/data pipeline tools (e.g., Kafka, Spark)
Knowledge of monitoring tools for ML systems (e.g., model drift, performance tracking)
Experience with Infrastructure as Code (Terraform/CloudFormation)
Required Skills
DEVOPS ENGINEER
SENIOR EMAIL SECURITY ENGINEER
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