Encore AI builds conversational AI agent platforms for banks, insurers, lenders, and investment houses across Israel, Australia, and the US. We're looking for a Deployment Strategist to own the bridge between what our AI agents can do and what our clients actually need in production.
This is not a classic onboarding or support role. You'll sit at the intersection of client strategy, prompt/flow engineering, and delivery ownership — translating a bank's or insurer's operational reality into a working, production-grade AI agent, and translating field learnings back into product and platform improvements. You'll work across our client base in Israel, Australia, and the US.
What You'll Own
Deployment ownership end-to-end. Take a client engagement from kickoff through go-live and into steady-state, owning the outcome — not just the handoff.
Solution design with clients. Run discovery and design workshops with client stakeholders (product owners, compliance, ops leads) to translate business requirements into agent flows, node logic, and conversation design.
Hands-on configuration. Work directly in the Encore AI platform — flow JSON, conversation/collect/end nodes, prompt engineering — to build and refine agents, rather than only spec'ing requirements for someone else to build.
Executive-level communication. Present progress, risk, and roadmap to client stakeholders ranging from project leads to C-suite, across our Israeli, Australian, and US accounts.
Organizational readiness. Help clients (often regulated financial institutions) navigate the process and change-management side of adopting an AI agent — not just the technical rollout.
Field-to-product feedback loop. Be the connective tissue between what clients need and what Encore AI builds — surfacing patterns across accounts to Product and Engineering.
Risk and escalation management. Identify delivery risk early (timeline, scope, technical blockers) and manage it proactively with both client and internal stakeholders.
Reusable playbooks. Build documentation, QA frameworks, and implementation patterns that make the next deployment faster than the last.