Enterprise Architecture​/ML Summer​/Fall Co-Op

Skyworks is an innovator of high-performance analog semiconductors whose solutions are powering the wireless networking revolution. Through our broad technology expertise and one of the most extensive product portfolios in the industry, we are Connecting Everyone and Everything, All the Time.

At Skyworks, you will find a fast-paced environment with a strong focus on global collaboration, minimal layers of management, and the freedom to make meaningful contributions in a setting that encourages creative thinking. We value open communication, mutual trust, and respect. We are excited about the opportunity to work with you and glad you want to be part of a team of talented individuals who together are changing the way the world communicates.

The Co‑Op will be responsible for designing and implementing solutions that meet the needs of various business areas across Skyworks’ Enterprise in and around the Machine Learning space. The incumbent will work under senior architects in the Enterprise Architecture group and with different departments to determine how to best implement new technologies and improve existing ones with a focus on machine learning operations and cloud platforms.

Projects will be exciting and on modern platforms varying across ML/AI as well as data governance.

• Collaborate with stakeholders:
  Engage with business stakeholders, data scientists, software engineers, and other teams to understand their requirements and align machine learning initiatives with overall business goals.
• Integration and interoperability:
  Work on solutions that seamlessly integrate with existing enterprise systems, databases, and APIs. Collaborate with internal and external partners to enable smooth data flow and interoperability across systems, ensuring consistent and accurate inputs for machine learning models.
• Model deployment and monitoring:
  Define and implement robust processes for deploying machine learning models into production environments. Establish monitoring mechanisms to track model performance, identify anomalies, and trigger retraining or updates when necessary. Ensure models comply with regulatory and compliance standards.
• Risk assessment and mitigation:
  Identify potential risks and challenges related to machine learning operations, such as data privacy, security vulnerabilities, or ethical considerations. Propose and implement mitigation strategies to ensure compliance, data integrity, and model robustness.
• Continuous improvement:
  Continuously evaluate and optimize the machine learning operations infrastructure and processes to improve efficiency, reliability, and scalability. Stay informed about advancements in machine learning operations and recommend new tools, frameworks, or methodologies that can enhance the organization’s capabilities.
• Data operations:
  Build and support data flows for moving or sourcing data sets for training and inferencing. This includes understanding the data requirements, identifying the best methods for data transfer, and implementing the data pipelines.
• Model Onboarding:
  Assist in onboarding projects and models to our platform by supporting the creation of environments, integrations for model invocations, inference scripts, and testing and debugging models.
• RAG applications:
  Pioneer the creation of RAG applications by collecting the data needed and implementing solutions using LLMs, vector databases, and database indexing.

• Enrolled in a Bachelor’s or Graduate level program in Computer Science, Artificial Intelligence, Information Technology or related field
• Strong knowledge of machine learning concepts, frameworks and technologies, such as Tensor Flow, PyTorch, Scikit-learn, Sci Py, Num Py, Pandas, Hugging Face, Lang Chain, and OpenAI
• Experience with MLOps tools and practices, such as CI/CD, Docker, Kubernetes, and MLflow
• Familiarity with RAG and its applications. Including experience with LLMs, vector databases, Lang Chain, and database indexing
• Experience with SQL and No
  
  SQL database e.g. MSSQL and PostgreSQL
• Proficiency in designing and deploying machine learning models in cloud environments (e.g., Azure/Azure ML, AWS, GCP)
• Demonstrated…