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# AI/ML Engineer

- **Company:** Lumentum
- **Location:** San Jose, CA, United States
- **Experience:** Starter
- **Salary:** $67,600.0 - $96,550.0
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, Amazon Web Services, Data Analysis, Microsoft Azure, Cloud Computing, Machine Learning, NoSQL, Azure Machine Learning, SQL Databases, Data Processing, Google Cloud, Feature Engineering, Model Validation, Build Management, Information Technology, Machine Learning Operations, Natural Language Understanding, Data Pipelines
- **Published:** September 17, 2026
- **Apply:** https://www.indeed.com/viewjob?jk=3cf1bb863aa937f7

## About the Role

* Master's in Computer Science, Electrical Engineering, Applied Mathematics, Statistics, or a related quantitative field.
 * 0 to 2 years of professional experience in developing and deploying AI/ML solutions in a production environment.
 * Experience with data processing and manipulation using SQL and/or NoSQL databases.
 * Familiarity with cloud platforms (e.g., AWS, Azure, GCP) and their AI/ML services.
 * Excellent problem-solving skills and the ability to work independently and as part of a team.
 * Strong communication and presentation skills, with the ability to explain complex technical concepts to non-technical stakeholders.

## Description

* Develop, implement, and optimize machine learning algorithms and models for tasks such as predictive maintenance, anomaly detection, image processing, and natural language understanding.
 * Design and build robust data pipelines for training and inference, ensuring data quality and efficient processing.
 * Perform extensive data exploration, feature engineering, and model validation to ensure high-performance and reliable solutions.
 * Collaborate with cross-functional teams, including software engineers, data scientists, and product managers, to integrate AI/ML solutions into existing systems and new product development.
 * Document technical designs, development processes, and experimental results clearly and concisely.
 * Contribute to the continuous improvement of our MLOps practices, including model deployment, monitoring, and retraining strategies.


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