AI/ML Engineer, National Security, Public Sector

AI/ML Engineer, National Security, Public Sector

In this role, you will guide National Security customers on how to configure and deploy their Artificial Intelligence (AI) and Machine Learning (ML) solutions. You will support customer implementations of Google Cloud products through architecture guidance, best practices, data migration, capacity planning, implementation, troubleshooting, monitoring, etc. You will consult with customers on how to design their AI and machine learning solutions including development and deployment of ML models. You will travel to customer sites to deploy solutions and deliver workshops to educate and empower customers. You will work with Product Management and Product Engineering to drive excellence in Google Cloud products and features.

Minimum Qualifications:

• Bachelor's degree in Engineering, Computer Science, a related field, or equivalent practical experience.
• 6 years of experience with machine learning model development and deployment, frameworks (e.g., PyTorch, Tensorflow, Jax, Ray, etc.), AI accelerators (e.g., TPUs, GPUs), model architectures (e.g., encoders, decoders, transformers), and using machine learning Application Programming Interface (APIs).
• 6 years of experience in working with technical customers. Experience with Python.
• Active U.S. Government Top Secret Clearance.
• Ability to travel up to 30% of the time.

Preferred Qualifications:

• Experience in optimizing Large Language Model (LLMs) leveraging retrieval augmented generation (RAG) architectures and fine-tuning techniques.
• Experience in building solutions for National Security customers.
• Experience in working with multiple clouds.
• Experience in C++ programming language.
• Experience in Linux/Unix.
• Ability to lead design and implementation of AI-based solutions, web services, and debugging tools and experience containerizing ML workloads.

Responsibilities:

• Act as a trusted technical advisor to customers and solve Machine Learning tests.
• Create and deliver recommendations, tutorials, blog articles, sample code, and technical presentations adapting to different levels of business and technical stakeholders.
• Work with Customers, Partners, and Google Product teams to deliver solutions into production.
• Coach customers on the practical tests in ML systems like feature extraction/feature definition, data validation, monitoring, and management of features/models.