Engineering Team Lead – Appointment Scheduling

🕒 vor 27 Tagen

🍂 Massachusetts – Remote

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⏰ Vollzeit

🟠 Senior

🧑‍💻 Full-Stack-Entwickler

🦅 H1B-Visum-Sponsor

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👻 Geisterscore 10%

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🗣️🇺🇸🇬🇧 Englisch erforderlich

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Engageware

201 - 500 Mitarbeiter

Gegründet 2000

💼 Beratung

🏥 Gesundheitswesen

📦 Logistik

Consulting • Healthcare • Logistics

Engageware ist eine KI-gestützte Kundenbindungsplattform, die Organisationen dabei hilft, den Kundenservice zu automatisieren und den Umsatz durch Omnichannel-Interaktionen, die sowohl virtuell als auch menschlich unterstützt werden, zu steigern. Die Plattform bietet virtuelle Assistenten, konversationelle KI, Terminplanung (inklusive Integrationen für Unternehmen und Salesforce), Video-Banking, Lobby- und Warteschlangenmanagement sowie einheitliches Wissensmanagement, um den Kunden-Self-Service und die Effizienz der Mitarbeiter zu verbessern. Engageware bedient hauptsächlich Banken, Kreditgenossenschaften, Einzelhändler, Vermögensverwalter, Hochschulen und andere Unternehmen als sichere, skalierbare SaaS-Lösung.

Beschreibung

• Own the Appointment Scheduling team's planning, prioritization, delivery process, and day-to-day execution • Ensure the team ships reliably at enterprise scale • Grow and coach engineers at all levels, including senior and principal individual contributors • Partner with the Principal Engineer on architectural decisions and shipped product • Collaborate with Product and Design to solve customer problems • Own technical direction for the scheduling platform, including availability, booking, routing, reminders, and multiple customer channels • Modernize the mature scheduling core through pragmatic modernization, improved test coverage, and cleaner API surfaces • Shape and evolve data architecture and service boundaries for scale and AI extensibility • Maintain the platform's reliability standards • Lead adoption of an AI-augmented software development lifecycle across design, code generation, testing, code review, and legacy modernization • Establish guardrails for AI-assisted development with strong testing and CI/CD practices • Stay current on agentic development patterns, including MCP-based tooling, and improve team fluency • Introduce intelligent product capabilities such as conversational booking, availability recommendations, reduced no-show rates, and proactive engagement • Ensure AI features use verified availability data and are evaluated against clear success metrics • Partner with the Principal PM to align AI product initiatives with customer value and the scheduling roadmap

🎯 Anforderungen

• Track record of leading an engineering team to deliver process, planning, and outcomes while remaining technically hands-on • Experience shipping product, not only managing a team • Depth in JVM backend technologies, including Java, Spring Boot, and Java 17 • Experience with modern TypeScript front ends using React and/or Angular • Comfort with high-throughput services and relational data • Experience modernizing large, mature, business-critical systems without disrupting the business • Fluency with AI coding agents and AI-augmented development • Genuine product sense focused on the customer booking experience • Based in the United States • Willingness to travel occasionally for team and customer meetings • Prior scheduling, calendaring, availability modeling, or resource-optimization experience is nice to have • SQL and relational data depth is nice to have • Enterprise-scale API design and integration experience is nice to have • Background in financial services, banking, credit unions, healthcare, or retail technology is nice to have • Experience exposing platform capabilities to AI agents via MCP or similar patterns is nice to have • Familiarity with SQL Server, PostgreSQL, Scala/Akka, Kubernetes on AWS, Claude models, and MCP-based agent tooling is not required but relevant to the role

🏖️ Vorteile

• Willingness to travel occasionally for team and customer meetings • AI-augmented development lifecycle and exposure to AI agent tooling • Opportunity to work with Claude models and MCP-based agent tooling

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