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# AI/ML Engineer / Data Scientist

- **Company:** Ampcus Inc
- **Location:** Jersey City, NJ, United States
- **Experience:** Expert
- **Contract:** Temporary to permanent
- **Skills:** Java (Programming Language), Artificial Intelligence, Amazon Web Services, Computer Vision, Big Data, Program Optimization, Computer Programming, Continuous Integration, Data Structures, Distributed Computing Environment, Distributed Systems, Python (Programming Language), Machine Learning, Natural Language Processing, Performance Tuning, Tensorflow, Software Engineering, User-Centered Design, Pytorch, Large Language Models, Deep Learning, Containerization, Scikit Learn, Kubernetes, Information Technology, Machine Learning Operations, Docker
- **Published:** August 28, 2026
- **Apply:** https://www.dice.com/job-detail/a5a7c38e-1a58-4b53-a32c-04d7055a1eb0

## About the Role

Bachelor's or Master's degree in Computer Science, Data Science, Engineering, or a related field.

 - **10+ years** of hands-on experience deploying machine learning models in production, with a track record of delivering large-scale systems.
 - Expert-level programming skills in **Python**, with basic understanding in additional languages such as Java, Scala.
 - Advanced understanding of data structures, algorithms, distributed systems, and software engineering principles.
 - Extensive experience with cloud platforms (AWS) and containerization/orchestration (Docker, Kubernetes).

-Deep experience with MLOps tooling (MLflow, SageMaker, Vertex AI) and CI/CD for ML.

 - Proven experience designing and maintaining large-scale data processing systems.
 - Demonstrated experience leading technical projects and mentoring engineers., Advanced knowledge of deep learning, NLP, computer vision, or large language models.
 - Experience with distributed training, model optimization, and high-throughput serving infrastructure.
 - Understanding of ML frameworks such as **TensorFlow, PyTorch, or scikit-learn**.

## Description

Lead the end-to-end design, development, and deployment of scalable machine learning models and systems in production.

 - Partner with data scientists to operationalize advanced research into reliable, production-ready services.
 - Define and implement monitoring, observability, and automated retraining strategies to ensure model reliability and detect drift.
 - Collaborate with product, engineering, and business leadership to shape ML roadmaps and translate strategic goals into technical deliverables.
 - Evaluate and introduce emerging ML technologies, frameworks, and methodologies to keep the organization at the forefront of innovation.
 - Own the technical health of ML systems, including performance optimization, cost efficiency, and scalability.


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