Principal Enterprise AI Engineer

If you are looking for a challenging and exciting career in the world of technology, then look no further. 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.

Requisition ID: 74855 

Position Summary

The Enterprise AI Engineer position 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.

Detailed Description

Responsibilities will include, but not be limited to:

• Stakeholder Collaboration: Partner with business stakeholders, data scientists, software engineers, and cross-functional teams to gather requirements and align machine learning and GenAI projects with strategic business objectives. Facilitate clear communication to ensure that developed models and solutions address the specific needs and expectations of all parties. Provide technical guidance to non-technical stakeholders to help them understand the potential and constraints of AI solutions.
• System Integration and Interoperability: Develop solutions that seamlessly integrate with existing enterprise systems, databases, and APIs. Collaborate with internal and external partners to ensure smooth data flow and system interoperability, providing consistent and accurate inputs for machine learning and GenAI models. Implement APIs and microservices to make AI functionalities accessible across various platforms and applications.
• Model Development and Optimization: Design, develop, and fine-tune traditional machine learning models and large language models (LLMs) such as GPT, BERT, and other GenAI frameworks. Leverage transfer learning and domain adaptation techniques to tailor pre-trained models to specific business use cases, ensuring optimal performance and relevance.
• Advanced NLP and GenAI Techniques: Apply state-of-the-art NLP techniques including text preprocessing, tokenization, named entity recognition (NER), sentiment analysis, text classification, and language generation. Fine-tune pre-trained models for specialized NLP tasks and applications to meet business needs.
• Traditional Machine Learning Techniques: Implement and optimize traditional machine learning techniques such as supervised and unsupervised learning, regression, classification, clustering, and ensemble methods. Conduct thorough model evaluation and hyperparameter tuning to achieve high performance and accuracy.
• Risk Management and Mitigation: Identify and address potential risks and challenges related to machine learning and GenAI, including data privacy, security vulnerabilities, and ethical considerations. Develop and implement strategies to mitigate these risks, ensuring compliance, data integrity, and model robustness. Regularly conduct audits and apply bias detection and mitigation techniques to ensure fair and unbiased model outcomes.
• Continuous Improvement: Continuously assess and enhance machine learning and GenAI models to improve accuracy, efficiency, and scalability. Stay current with the latest advancements in machine learning, NLP, and GenAI, and recommend new tools, frameworks, or methodologies to enhance organizational capabilities. Engage in research and development (R&D) activities to explore innovative AI techniques and their potential business applications.
• Data Pipeline Development: Design and maintain data pipelines for sourcing and processing datasets required for training traditional ML, NLP, and GenAI models. Ensure data quality and consistency through rigorous data cleaning, transformation, and augmentation processes. Optimize data flows to meet model training requirements efficiently.

Detailed Description (Additional)

• Model Depl