• Lead the technical design and delivery of AI-native accessioning automation services across paper and electronic requisition forms. • Build fast, ship independently. • Apply AI pragmatically using LLMs, vision models, OCR, and IDP frameworks. • Own the product outcome by partnering with Accessioning Operations, Product, and Engineering teams. • Integrate across systems and build into existing mature commerce and fulfillment systems. • Set the bar for engineering practices on a new team: AI-assisted development, strong test automation, CI/CD, and observability. • Evaluate new AI tools, models, and vendor solutions. • Mentor and multiply the technical quality of peers.

• Design, develop, and maintain production‑grade software systems with a focus on performance, reliability, and scalability • Optimize system behavior for real‑time, low‑latency environments, including edge and cloud components • Design, implement, and refine core system logic that improves platform performance, reliability, and scalability under real‑world conditions. • Profile, analyze, and optimize system performance across CPU, memory, I/O, and networking • Improve system observability through logging, monitoring, and diagnostics • Work on end‑to‑end development, from design and implementation to testing, deployment, and support • Collaborate with Deployment, ML, and Systems teams to validate and support improvements in production‑like and live environments. • Debug and resolve complex production issues involving distributed or real‑time systems • Write clean, maintainable, and well‑tested code following engineering best practices • Participate in code reviews and contribute to improving engineering standards and tooling • Support production systems post‑deployment and assist with performance tuning and issue resolution

• Design and implement robust authentication and authorization systems using industry-standard protocols such as OAuth 2.0, SAML, and JWT • Develop and maintain secure admin panels and dashboards that enable efficient system administration and user management • Architect microservices-based solutions for identity and access management, ensuring scalability and high availability • Conduct thorough security reviews and implement best practices to protect against vulnerabilities and threats • Collaborate with cross-functional teams across distributed locations to define requirements, establish technical standards, and drive implementation of authentication solutions • Partner with Product teams to understand customer needs and business objectives, translating them into technical solutions that balance security, usability, and scalability • Mentor junior and mid-level engineers, providing technical guidance and code reviews to ensure code quality and knowledge sharing • Analyze system performance, identify bottlenecks, and optimize authentication workflows for efficiency and reliability • Participate in on-call rotations and respond to security incidents with decisive action and technical expertise • Document system architecture, design decisions, and operational procedures to ensure organizational knowledge retention • Stay current with emerging security trends, authentication technologies, and industry compliance standards • Contribute to the evaluation and integration of third-party identity management tools and services

• Design and implement agentic workflows that materially reduce delivery time and cognitive load • Build reusable accelerators, templates, and shared services for engineers, product managers, designers, and analysts • Apply orchestration tools (LangChain, LangGraph, LangSmith, MCPs, n8n) with judgment—knowing where agents add leverage and where simpler solutions win • Define and evangelize AI-first development practices across teams • Leverage AI-enabled development environments (Cursor, Windsurf) and establish best practices for human + AI collaboration • Drive measurable step-changes in team velocity and consistency • Architect and Build LLM- and ML-powered solutions supporting digital journeys, call-center workflows, decisioning, and personalization • Develop end-to-end systems using Python, JavaScript/TypeScript (optionally Go) • Partner with Data Science to integrate generative AI with traditional ML (classification, regression) • Build AI-enabled interfaces using modern web stacks (React, Next.js, TypeScript) • Ensure scalability, reliability, security, and observability • Design modular, reusable AI systems that scale across clients and use cases • Build and operate production LLM systems, including RAG and agent-based architectures • Use Databricks for AI development (Spark, Delta Lake, MLflow) • Apply strong engineering discipline: Git workflows, code reviews, TDD, CI/CD • Mentor engineers and shape shared standards, patterns, and documentation