Enterprise Architecture Summer Intern

Requisition ID: 72601 

Summary

The Enterprise Architecture summer intern 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 intern 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.

Description

Responsibilities will include, but not be limited to:

• 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 LLM’s, vector databases, and database indexing.

Requirements

Enrolled in a Bachelor’s or Graduate level program in Computer Science, Artificial Intelligence, Information Technology or related field

Available for the full summer duration. Opportunity to convert the internship into a Co-Op.

Strong knowledge of machine learning concepts, frameworks and technologies, such as TensorFlow, PyTorch, Scikit-learn, SciPy, NumPy, Pandas, Hugging Face, LangChain, 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, LangChain, and database indexing

Experience with SQL and NoSQL 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 expertise in architecting scalable and secure machine learning infrastructure, including data pipelines, storage systems, and model deployment frameworks

Excellent communication and collaboration skills, with the ability to effectively engage with stakeholders at various levels of the organization

Ability to multitask and manage multiple activities simultaneously

Ability to use a wide degree of creativity and latitude to think differently, challenge conventional wisdom, and drive new best practices

Ability to work effectively with international teams