• Overhaul how Close syncs email and calendars. Our sync engine has run for a decade; we're making a serious, ground-up investment in its performance and reliability. • Take voice agents further into the call. We own the live conversation layer for Chloe's calls, and there's a lot of work ahead expanding our voice agent capabilities and infrastructure. • Solve inbound. So far Chloe makes calls; next she answers them. An inbound conversation can go anywhere — which raises real questions about specialized agents, a receptionist layer, and seamless call routing between them. • Build native scheduling. Core calendar scheduling capabilities inside Close, plus more powerful scheduling for our agents. • Design human orchestration of an agentic workforce. Agents create work for humans too — things to review, approve, respond to. • Push connect rates and answer rates. Phone number pooling, local presence, branded calling, reputation monitoring. • Move our streaming and event substrate forward. Kafka, Redis, pub/sub.

• Ship code execution for the assistant. The assistant decides when writing code beats answering non-deterministically — today that's generating charts and tables in Python on the fly; next it's reusable user-defined tools and calling external APIs to pull in whatever data the task needs. • Build the eval and observability layer that tells us when an agent is getting better. Unit tests don't cut it for non-deterministic output. We run evals and tracing (LangFuse and our own tooling) as the bar for shipping: if we can't measure it, we don't ship it. • Push generated UI forward. The backend increasingly decides what the user sees — the LLM picks the right presentation (table, chart, widget) and renders it in the assistant, with a full-screen experience and stored, referenceable artifacts on the roadmap. Effectively: customers generate the reports we used to hand-build, one custom report at a time. • Take Custom Agents from prototype to GA. Event-driven agents that act on what's happening inside the CRM in real time — an email lands, an agent drafts the reply from knowledge sources and context, the user approves. This is where we differentiate from the general-purpose assistants: we see the events, we have the context. • Make deterministic and non-deterministic systems work together. Sales processes need steps that happen every single time; LLMs are bad at that. You'll help fuse our Workflows engine with agentic steps so customers get reliability where it matters and intelligence where it helps. • Handle the edges that make agents trustworthy. What happens to a fleet of running agents when a customer's AI credits run out? Pause semantics, recovery, and making sure nothing places a hundred calls that were supposed to happen last week. • Pick the right model for the job. We use many providers, test new models constantly, and are moving toward cost-aware routing — simple summarization jobs shouldn't run on frontier-priced models. You'll call when something is production-ready and when it's still a demo.

• Design and develop robust, scalable Java applications • Collaborate with teams to lead software architecture decisions • Mentor junior developers and facilitate knowledge sharing • Ensure high performance and responsiveness of applications • Participate in code reviews and maintain coding standards

• Architectural Migration: Lead the technical strategy, design, and execution of extracting deep business logic from existing Oracle PL/SQL stored procedures and re-architecting them into highly modular .NET 8 microservices. • Cloud & DevOps Leadership: Design and oversee the deployment architecture within a cloud environment, primarily leveraging AWS EKS (Elastic Kubernetes Service) and AWS Service Mesh (or equivalent cloud native tools). • Team Technical Leadership: Provide direct mentorship, technical direction, and code/design review guardrails for a team of 5 backend engineers. • Design & Best Practices: Define microservices patterns, database communication strategies, data validation frameworks, and performance-tuning guidelines for the new ecosystem. • Cross-Functional Collaboration: Act as the primary technical point of contact during the migration, bridging the gap between legacy database structures and modern cloud-native architectures.