• Introduce clients to the intersection of AI and NetSuite • Find leads and book solution meetings • Execute agreements • Manage and juggle different projects • One position includes expanding Shopify practice and selling stores

• Partner with customers to build and deploy impactful Gen AI and machine learning solutions, from use case scoping and data exploration to model development and deployment. This may involve leveraging Snorkel Flow or designing custom approaches using state-of-the-art tools, with the goal of delivering real business value and informing the evolution of the Snorkel platform. • Develop and implement state of the art AI systems such as retrieval-augmented generation (RAG), fine-tuning pipelines, prompt engineering recipes and agentic workflows. • Create augmented real-world datasets and comprehensive evaluation workflows to ensure model reliability, transparency, and stakeholder trust. A data- and evaluation-first mindset is essential for success in this role. • Forge and manage relationships with our customers’ leadership and stakeholders to ensure successful development and deployment of AI projects with Snorkel Flow. • Collaborate closely with pre-sales Solutions and Product teams to map customer needs to existing capabilities, prioritize roadmap gaps, and guide successful project setup. • Work with other Applied AI Engineers to standardize solutions and contribute to internal tooling and best practices. • Lead stakeholder education on quantitative capabilities, helping them to understand the strengths and weaknesses of different approaches and what problems are best-suited for Snorkel AI. • Serve as the voice of our customers for new AI paradigms, data science workflows, and share customer feedback to product teams. • Conduct one-to-few and one-to-many enablement workshops to transfer knowledge to customers considering or already using Snorkel AI. • Annual travel up to 25%.

• Design, develop, and deploy AI-driven applications and services, with a strong focus on Machine Learning and Generative AI use cases. • Build and integrate LLM-based solutions using frameworks such as LangChain, ensuring reliability, scalability, and performance. • Collaborate with product managers, designers, and data teams to translate business requirements into technical AI solutions. • Implement data pipelines, model training workflows, and inference services across different environments. • Optimize models and systems for performance, cost, and scalability in production. • Ensure best practices related to model evaluation, monitoring, versioning, and responsible AI usage. • Contribute to technical discussions, architectural decisions, and proof-of-concepts for AI initiatives. • Stay up to date with emerging AI technologies, tools, and industry trends.

• Design and deliver AI-driven systems that power features used by millions of users • Build scalable AI systems that connect models, data, and products • Turn research prototypes into reliable, production-ready services • Build pipelines and serving layers that power adaptive, real-time features • Collaborate with other engineering teams to ensure performance and reliability • Create reusable SDKs and libraries that accelerate AI adoption