ShiftKey

• Lead and grow a distributed platform and infrastructure organization, staying hands-on with the team when issues arise. • Drive infrastructure modernization and cloud migration initiatives, including moving services to managed cloud offerings. • Own reliability, deployment, and disaster recovery practices across the organization's core environments. • Set engineering standards for infrastructure automation, CI/CD, and operational resilience. • Partner with engineering and senior leadership to translate platform strategy into practical roadmaps.

• Lead and grow a distributed platform and infrastructure organization, staying hands-on with the team when issues arise. • Drive infrastructure modernization and cloud migration initiatives, including moving services to managed cloud offerings. • Own reliability, deployment, and disaster recovery practices across the organization's core environments. • Set engineering standards for infrastructure automation, CI/CD, and operational resilience. • Partner with engineering and senior leadership to translate platform strategy into practical roadmaps.

• Evolving the AI knowledge platform - taking the retrieval, indexing, and synthesis layer (currently semantic RAG + re-ranking + HyDE) to an organization-wide platform serving both internal engineering tools and customer-facing capabilities. • Architecting and operating agentic infrastructure on AWS - multi-step, tool-using AI systems that plan, retrieve, and act on complex queries and operational events, with cost guardrails and observability built in from day one. • Designing and building graph-based, relationship-aware retrieval across the organization's data sources, enabling multi-hop queries and letting agents accumulate organizational knowledge over time. This is on our roadmap, not in production - you will define the approach. • Partnering with product engineering to define the AI platform API surface, translating infrastructure primitives into developer-ready abstractions. • Building reference agent implementations on the platform - operational-incident triage, customer support, and future agentic use cases - grounding each agent's reasoning in institutional knowledge. • Owning the AI infrastructure cost model: monitoring compute, model, and storage spend, flagging anomalies, and proposing guardrails to keep workloads within defined budgets.

• Evolving the AI knowledge platform - taking the retrieval, indexing, and synthesis layer (currently semantic RAG + re-ranking + HyDE) to an organization-wide platform serving both internal engineering tools and customer-facing capabilities. • Architecting and operating agentic infrastructure on AWS - multi-step, tool-using AI systems that plan, retrieve, and act on complex queries and operational events, with cost guardrails and observability built in from day one. • Designing and building graph-based, relationship-aware retrieval across the organization's data sources, enabling multi-hop queries and letting agents accumulate organizational knowledge over time. This is on our roadmap, not in production - you will define the approach. • Partnering with product engineering to define the AI platform API surface, translating infrastructure primitives into developer-ready abstractions. • Building reference agent implementations on the platform - operational-incident triage, customer support, and future agentic use cases - grounding each agent's reasoning in institutional knowledge. • Owning the AI infrastructure cost model: monitoring compute, model, and storage spend, flagging anomalies, and proposing guardrails to keep workloads within defined budgets.

• Lead backend development on our core scheduling platform modernization - setting Python best practices, guiding architecture decisions, and owning the quality of Python-driven services as the system evolves. • Contribute across a multi-product ecosystem (Engage, Auth, Messaging, Time) that spans Python and .NET - this role is built for engineers who thrive across stacks, not single-use specialists. • Write APIs, own services review code, and ship - the full range of what senior engineering looks like in practice. • Help drive the team’s evolution toward an AI-accelerate SDLC, using tools like Claude Code to orchestrate agents that write, debug, and test software - this is an active part of how we work, not a future ambition.

• Lead backend development on our core scheduling platform modernization - setting Python best practices, guiding architecture decisions, and owning the quality of Python-driven services as the system evolves. • Contribute across a multi-product ecosystem (Engage, Auth, Messaging, Time) that spans Python and .NET - this role is built for engineers who thrive across stacks, not single-use specialists. • Write APIs, own services review code, and ship - the full range of what senior engineering looks like in practice. • Help drive the team’s evolution toward an AI-accelerate SDLC, using tools like Claude Code to orchestrate agents that write, debug, and test software - this is an active part of how we work, not a future ambition.

• Drive professional onboarding experience from registration and identity verification to credential collection and first-shift readiness. • Focus relentlessly on accelerating how fast a professional can move from signing up to actively and compliantly working on the platform. • Move beyond basic UI improvements by actively incorporating AI-native workflows and agentic solutions to solve manual, high-friction onboarding steps. • Act as the most demanding, well-specified consumer of our internal credentialing rules engine, ensuring onboarding gets what it needs to create a fast, clear, and confidence-building user experience.

• Drive professional onboarding experience from registration and identity verification to credential collection and first-shift readiness. • Focus relentlessly on accelerating how fast a professional can move from signing up to actively and compliantly working on the platform. • Move beyond basic UI improvements by actively incorporating AI-native workflows and agentic solutions to solve manual, high-friction onboarding steps. • Act as the most demanding, well-specified consumer of our internal credentialing rules engine, ensuring onboarding gets what it needs to create a fast, clear, and confidence-building user experience.

• Drive professional onboarding experience from registration and identity verification to credential collection and first-shift readiness. • Focus relentlessly on accelerating how fast a professional can move from signing up to actively and compliantly working on the platform. • Move beyond basic UI improvements by actively incorporating AI-native workflows and agentic solutions to solve manual, high-friction onboarding steps. • Act as the most demanding, well-specified consumer of our internal credentialing rules engine, ensuring onboarding gets what it needs to create a fast, clear, and confidence-building user experience.

• Drive professional onboarding experience from registration and identity verification to credential collection and first-shift readiness. • Focus relentlessly on accelerating how fast a professional can move from signing up to actively and compliantly working on the platform. • Move beyond basic UI improvements by actively incorporating AI-native workflows and agentic solutions to solve manual, high-friction onboarding steps. • Act as the most demanding, well-specified consumer of our internal credentialing rules engine, ensuring onboarding gets what it needs to create a fast, clear, and confidence-building user experience.