AI Engineer, Salesforce

• This role demands a strong foundation in engineering, data science, natural language processing (NLP), and prompt engineering, with a focus on extracting the maximum potential from LLMs
• Bachelor’s, Master’s, or Ph.D. in Computer Science, Data Science, Machine Learning, or a related field
• Strong expertise in LLMs/MLMs (e.g., GPT, BERT, T5, Llama) and prompt engineering techniques
• 5+ years of professional software development experience
• High proficiency in Java programming language
• Experience in automated testing including unit and functional testing using Java, JUnit, JSUnit
• Experience in REST-based API development, API lifecycle management and/or client SDKs development
• Solid understanding of both relational and non-relational databases
• Problem Solving and Communication:
• Ability to translate business needs into AI solutions and communicate complex technical ideas to non-technical stakeholders
• Proven experience working in cross-functional teams to deliver impactful results
• Ability to effectively communicate and execute with geographically distributed teams
• Experience in a fast-paced technical environment with changing priorities
• Ability to develop and ship products in quick increments across multiple versions

Desirable

• As an AI Engineer with expertise in using Large Language Models (LLMs) and Prompt Engineering, you will drive innovation by designing, fine-tuning, and optimizing the use of LLMs for a variety of enterprise use cases
• You will work at the intersection of research and practical implementation, developing state-of-the-art solutions to transform how organizations leverage AI in their workflows
• You will collaborate across interdisciplinary teams to integrate cutting-edge AI technologies into Salesforce’s product ecosystem
• Backend Development:
• Design, implement, and tune robust APIs and API framework-related features that perform and scale in a multi-tenant environment
• Design and develop generic, customer-facing objects that promote both ease of use and customization
• Prompt Engineering and Optimization:
• Design and experiment with creative prompts to optimize the performance of LLMs for specific use cases, ensuring accuracy, relevance, and efficiency
• Implement techniques like few-shot learning, chain-of-thought prompting, and context tuning to enhance LLM outcomes
• LLM Prompt Fine-Tuning:
• Use pre-trained LLMs (e.g., GPT, Llama, Falcon) for Salesforce-specific applications using prompt engineering and reinforcement learning from human feedback (RLHF)
• Build and maintain datasets for training, fine-tuning, and evaluation of LLMs for various use cases
• AI Model Evaluation and Deployment:
• Develop metrics and tools to evaluate model performance, reliability, and fairness
• Deploy LLM prompts into production systems, ensuring scalability, security, and efficiency
• Collaboration and Research:
• Collaborate with product managers, engineers, and designers to identify and implement AI-driven solutions
• Stay at the forefront of LLM research and emerging technologies to recommend and integrate advancements into Salesforce’s AI strategy
• Participate in the team’s on-call rotation to address complex problems in real-time and keep services operational and highly available
• Data Science and Insights:
• Analyze large-scale datasets to uncover patterns, insights, and opportunities for AI applications
• Ensure data quality, security, and compliance with industry standards