• Lead Engineering from Concept to Launch • Own technical architecture, design, and implementation for new AI driven insurance products • Translate open ended opportunities into defined system designs and deliver production grade solutions • Make foundational decisions related to architecture, infrastructure, data flows, and scaling • Develop prototypes and production features using Python, modern frameworks, LLM APIs, vector databases, and cloud native services • Write full stack code with a focus on quality, reliability, and performance • Participate in customer sessions to observe workflows and validate product direction.

This description is a summary of our understanding of the job description. Click on 'Apply' button to find out more. Role Description We’re looking for a Forward Deployed AI Architect to lead our most mission-critical client engagements. This is not a lab role - you’ll be embedded with enterprise clients, building and deploying AI solutions where the problems are messy, the stakes are high, and the environment is rarely “ideal.” You’ll be the one who makes things work in the real world: taking designs from architects, wrestling them into production, and ensuring they scale reliably under real constraints. If you thrive on chaos, love solving problems end-to-end, and want your code to matter outside of demos, this role is for you. Key Responsibilities - Client Engagement & Delivery: - Work side by side with clients, PMs, and Architects to scope and deploy AI systems that actually solve problems. - Build and integrate systems using LLMs, RAG pipelines, agent frameworks, vector DBs, and related tools. - Navigate real-world challenges: data access, authentication, security, networking, and brittle enterprise environments. - Solution Implementation: - Translate high-level designs into working components that deliver measurable outcomes. - Debug relentlessly - optimize for reliability in production, not just elegance in code. - Write modular, reusable code that balances speed of delivery with long-term maintainability. - Collaboration & Enablement: - Embed with client engineering teams to accelerate adoption and integration. - Share insights from field implementations to inform reusable components in our platform. - Document decisions with clarity so both clients and internal teams can build on your work. Qualifications - 5+ years of engineering experience, with proven work shipping AI/ML systems into production. - Strong Python skills and deep familiarity with modern AI/ML frameworks (LangChain, LlamaIndex, Hugging Face, vector DBs). - Hands-on with cloud deployment, orchestration, CI/CD, and distributed environments (AWS, Azure, GCP). - Ability to debug gnarly real-world issues (auth failures, networking black holes, flaky data pipelines). - Problem-solver who thrives in ambiguity, focused on delivery and reliability over process and ceremony. - Comfortable in client-facing environments - credible with engineers, clear with executives. Benefits - Impact: Ship AI systems that don’t just demo well but run at scale in Fortune 500 enterprises. - Growth: Stay hands-on with cutting-edge frameworks while developing field-tested instincts. - Variety: Solve problems across industries, from finance to healthcare to defense. - Culture: Work in a team that prizes resilience, creativity, and winning over process. - Trajectory: Build both your technical and consulting muscles in one of the most demanding roles in AI delivery.

• 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%.