• Own the AI roadmap. • Set direction across our AI surfaces: voice and computer-use agents that work payers and provider portals, email triage, and the models that extract and structure data from clinical and legal documents. • Push AI deeper. • Take our systems from assisting people, to making decisions, to running whole service requests, and into licensing, enrollment, monitoring, and the customer-facing product. • Make it production-grade. • Build the evals, observability, and human-in-the-loop systems that let us ship models and agents we'll stand behind and back with guarantees. • Lead the data platform. • Own the pipelines, datasets, and tooling behind the models. Our real-world data is our biggest advantage, so make it fast to use and trustworthy to build on. • Build the org. • Hire and grow managers and senior engineers, set the structure, and hold the bar as we scale. Stay close enough to the work to earn their trust in a design review.

• Design & Develop Agentic Systems: Build intelligent agents capable of autonomous planning, reasoning, and task execution, often using LLMs (e.g., GPT-class, LLaMA), multi-modal models, and autonomous workflows • Orchestration & Frameworks: Implement agent orchestration using frameworks like LangChain, AutoGen, CrewAI, Semantic Kernel, or custom solutions • Retrieval-Augmented Generation (RAG): Design and optimize RAG pipelines for enhanced reasoning with external knowledge, including document ingestion, chunking, embeddings, vector stores, and retrieval ranking • Tool & Memory Integration: Develop agents that call APIs, databases, and other tools, maintain memory, and adapt based on outcomes • Evaluation & Monitoring: Create evaluation frameworks for accuracy, grounding, latency, and cost; build observability for agent behavior and failure modes • Model Adaptation: Fine-tune or adapt foundation models (e.g., via LoRA, adapters) for domain-specific use cases • Production Deployment: Deploy GenAI/agentic systems in cloud-native environments with CI/CD, versioning, and runtime safeguards • Cross-Functional Collaboration: Work with data scientists, ML engineers, product teams, and governance/compliance stakeholders

• Lead the architecture, design, and engineering delivery of the organization's agentic AI platform and related solutions • Drive the transformation of existing products, processes, and workflows into scalable agentic AI-enabled capabilities • Oversee end-to-end AI engineering operations, including development, deployment, monitoring, reliability, and continuous improvement • Partner with cross-functional stakeholders to define technical roadmaps, platform standards, and solution priorities aligned to business goals • Establish engineering best practices for AI system performance, scalability, security, governance, and maintainability

Role Description We're launching a program for experienced engineers transitioning into system architecture roles. The curriculum is being built by a team of senior authors. What we need now is the person who brings it to life for students: - Host the live sessions - Review the work - Run the architecture review boards - Set the technical bar for the people supporting alongside you This is a teaching and review role. You'll be the senior technical presence the cohort learns from week to week: - Run live sessions and office hours - Give real architectural feedback on student deliverables - Act as the escalation point for a team of supporting instructors Your audience: experienced engineers leveling up to system architecture roles. Many already write production code daily. They will spot shallow feedback instantly, so the bar here is real seniority, not familiarity. Format: Async-first with scheduled live sessions. Live sessions and office hours are timed around a US-based cohort. Estimated 15 to 25 hrs/week. Please submit all resumes or CV's in English. Qualifications - 5+ years as a senior software/systems architect, platform engineer, or senior backend/infrastructure engineer - Has designed and shipped systems at scale in a real company, not just coursework or side projects - Depth across the architectural spine: system design, API and service architecture, cloud and infrastructure (AWS, Kubernetes, Terraform, CI/CD), and distributed systems - Can explain why a decision was made, not just how it was implemented, and can diagnose and critique someone else's architecture live - Strong technical communication: comfortable leading a live session and writing clear, specific review feedback - Strong English C1+ — instruction and review are in English for a US-based audience - Comfortable using AI tools in day-to-day technical work Requirements - Host weekly live sessions across the program, focused on the design and decision layer - Run group office hours and 1:1 sessions for students working through projects and the capstone - Review student projects against rubrics: the architecture dossiers, deployed backends, cloud infrastructure, performance reports, security reviews, and the final distributed-system capstone - Give architectural feedback, not just pass/fail checks - Run the mock architecture review boards and the simulated executive-panel presentations - Set the technical standard for a team of supporting instructors Benefits - Milestone-based payments tied to deliverables - Fully remote work with flexible hours - A digital office — we use modern tools (Notion, Miro, Slack, Figma) to keep collaboration smooth - Professional trust and autonomy — no micromanaging - The chance to see real impact: our metric is graduates landing jobs in tech - Join a diverse, international, and close-knit team excited to work with you