AI/ML Engineer ____ Lebanon, NJ - 08833/ NY, NY - 10010(Onsite)___Full Time Employment
Role summary
We are seeking a skilled AI/ML Engineer to design, build, and deploy scalable machine learning solutions. This role requires strong experience with the AWS ML ecosystem, MLOps, and production-grade model deployment, with a preference for candidates with domain exposure in Insurance (Claims, Underwriting, Fraud Detection). Key responsibilities include developing end-to-end ML pipelines, managing ML workflows using Amazon SageMaker, implementing MLOps best practices, deploying models as scalable inference endpoints, and containerizing applications with Docker and Kubernetes. The ideal candidate will be proficient in Python and ML libraries, with experience in version control and cloud architecture.
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)