AI/ML Engineer-3
Realign
About the role
New York City, New York 10010 Posted March 28th, 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
Requirements
- 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)**
- *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
- Experience in the **Insurance domain**, including:
- 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)
Responsibilities
- We are looking for a skilled **AI/ML Engineer** to design, build, and deploy scalable machine learning solutions
- 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
Benefits
Skills
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