AI Architect

Salesforce•San Francisco, CA

11h•Hybrid

About The Position

Salesforce is seeking an AI Architect to join their dynamic team focused on exploring, developing, and integrating generative AI and applied AI technologies across their products and operations. This role offers a unique opportunity to lead and contribute to innovative projects that will redefine how internal teams leverage AI to enhance productivity, drive insights, and create new capabilities. The AI Architect will be a key member of the Engineering Development and Delivery Applied AI team, instrumental in identifying, evaluating, and implementing AI-driven solutions to address critical business challenges and unlock new opportunities within Salesforce. The focus will be on bridging the gap between advanced AI research and practical application, particularly leveraging generative models, large language models (LLMs), and other applied AI techniques to create intelligent tools, automate complex tasks, and generate novel content or insights. This role involves close collaboration with product, engineering, and various internal stakeholders.

Requirements

At least 15 years of programming and architectural experience.
Strong proficiency in Python and relevant AI/ML frameworks.
Knowledge of various machine learning models, including deep learning architectures, particularly those related to natural language processing (NLP) and generative models.
Solid understanding of cloud platforms, specifically AWS, and MLOps principles.
Familiarity with core AI/ML concepts, including data preprocessing, feature engineering, model training, evaluation, and deployment.
Practical experience working with Large Language Models (LLMs), fine-tuning, prompt engineering, and understanding their applications for text generation, summarization, code generation, and conversational AI.
Capable of reading through AI research and understanding approaches for how to apply new concepts.
A strong understanding of developing and delivering services on cloud platforms like AWS, including concepts around cloud security, identity and access management, data protection, and network security.
Hands-on experience designing, integrating, or utilizing autonomous AI coding agents and developer platforms (such as Salesforce Agentforce) to streamline software development lifecycles, automate workflow execution, and enhance developer productivity.
Strong verbal and written communication skills, capable of explaining complex technical concepts to non-technical stakeholders and providing content that can be understood by both humans and models.
Proven ability to work effectively in a collaborative, fast-paced team environment where requirements are light and ambiguity is high.
Must be capable of leading through the ambiguity and into durable solutions.
Bachelor's or Master's degree in Computer Science, Artificial Intelligence, Machine Learning, Data Science, or a related technical field.

Nice To Haves

Experience with Salesforce platform technologies (specifically Apex, Agentforce, and Data Cloud).
Prior experience in a leadership or mentorship role within an engineering development team.
Familiarity with data governance, security, and privacy best practices in an enterprise context.

Responsibilities

Contribute to the strategic direction for generative AI and applied AI initiatives within Salesforce, identifying high-impact areas for innovation and development.
Lead the design, development, and deployment of generative AI and applied AI prototypes and production-ready solutions, leveraging LLMs for code generation, content creation, intelligent search, data synthesis, and more.
Stay abreast of the latest advancements in generative AI, applied AI, and machine learning research and techniques, evaluating new models, tools, and platforms for potential application within Salesforce.
Partner with product managers, data scientists, engineers, and business stakeholders to gather requirements, define project scopes, and ensure successful integration and adoption of AI solutions.
Act as a subject matter expert, providing guidance and sharing knowledge with other teams on best practices, ethical considerations, and the capabilities of generative and applied AI.
Monitor and optimize the performance of deployed AI models, ensuring scalability, reliability, and continuous improvement.