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# Senior AI/ML Engineer

- **Company:** Insight Global
- **Location:** Newark, NJ, United States
- **Experience:** Expert
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, Amazon Web Services, Github, Python (Programming Language), Machine Learning, Software Deployment, Large Language Models, Generative AI, Cloudformation, Kubernetes, Information Technology, Virtual Agents, Terraform, Data Pipelines, Docker
- **Published:** June 5, 2026
- **Apply:** https://dejobs.org/x/x/2F2B662CAC154E43A452A9DF9B8A6467/job/

## About the Role

* Bachelor's degree in Computer Science, Engineering, Data Science, or related field
 * 5+ years as a Machine Learning Engineer with production deployment experience
 * Proficient in Python programming
 * Strong experience with AWS (SageMaker, Studio, Bedrock)
 * Skilled in Docker, Kubernetes, Terraform, and CloudFormation
 * Deep understanding of LLMs (LangChain, OpenAI preferred)
 * Experience with CI/CD pipelines (GitHub Actions)
 * Familiar with GenAI techniques like RAG, hallucination monitoring, and human-in-the-loop systems
 * Proven ability to deploy, monitor, and maintain GenAI models in production

Able to build and manage data pipelines and storage for model operations Master's degree in computer science/ engineering, data science or related field

## Description

We are seeking a highly skilled AI/ML Engineer to join the CDO organization and support Prudential's enterprise AI transformation. This role requires a strong foundation in traditional machine learning, along with hands-on experience designing and operationalizing Generative AI and Agentic AI solutions. In this role, you will design, build, and scale production-grade GenAI systems, partnering closely with business and technology stakeholders to translate use cases from proof of concept through full production deployment. Responsibilities include architecting large-scale AI platforms using historical data, deploying solutions in AWS environments, and ensuring systems meet enterprise standards for scalability, security, reliability, and cost efficiency. Success in this role depends on strong cross-collaboration with data scientists, engineers, and cross-functional teams, as well as the ability to bridge experimentation with production-ready execution.


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