As an AI Architect on the Engineering Development and Delivery Applied AI team, you will bridge the gap between advanced research and practical application by identifying, evaluating, and implementing AI-driven solutions to solve critical business challenges. You will lead the design and deployment of generative AI prototypes and production-ready tools for tasks like code generation, content creation, and data synthesis. The role requires extensive experience with Python, MLOps principles, and AWS cloud platforms. You will work with large language models (LLMs), fine-tuning, prompt engineering, and deep learning architectures focused on natural language processing. Key responsibilities include collaborating with cross-functional teams to define project scopes, monitoring model performance for scalability, and mentoring others on ethical considerations. This role focuses on integrating generative AI technologies into internal products to enhance productivity and automate complex workflows.
Identify, evaluate, and implement AI-driven solutions to address critical business challenges and opportunities.
Bridge the gap between advanced AI research and practical applications like LLMs for code generation and data synthesis.
Lead the design, development, and deployment of generative AI prototypes and production-ready tools.
Research and evaluate emerging machine learning models, tools, and industry research papers for internal use.
Partner with cross-functional teams to gather requirements and ensure successful integration of AI solutions.
Act as a subject matter expert to provide guidance on best practices and ethical considerations in AI.
Monitor and optimize the performance, scalability, and reliability of deployed AI models.
Utilize autonomous AI coding agents to streamline software development lifecycles and enhance developer productivity.
What we're looking for
Bachelor's or Master's degree in Computer Science, Artificial Intelligence, Machine Learning, Data Science, or a related technical field.
At least 15 years of programming and architectural experience.
Strong proficiency in Python and relevant AI/ML frameworks.
Knowledge of machine learning models, including deep learning architectures for natural language processing and generative models.
Solid understanding of cloud platforms (specifically AWS) and MLOps principles.
Practical experience with Large Language Models (LLMs), fine-tuning, prompt engineering, and their applications in text generation and conversational AI.
Hands-on experience designing or utilizing autonomous AI coding agents and developer platforms to streamline software development lifecycles.
Experience with Salesforce platform technologies, leadership roles, or enterprise data governance practices (preferred).
Terraform
Infrastructure as Code
AI Infrastructure
Data Pipelines
Machine Learning
Monitoring
Logging
Alerting
Networking
Storage
Version Control
Automation