AI/ML Engineer / Data Scientist
Ampcus Inc
Jersey City, NJ, United States
9 days ago
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Role details
Contract type
Temporary to permanent
Employment type
Full-time (> 32 hours)
Experience level
Expert
Experience required
10 years minimum
Working hours
Regular working hours
Job source
Tech stack
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)
+15 more
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
Job 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.
Requirements
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.
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