Parloa

Parloa

Senior/Lead Forward Deployed Engineer - US

New York Office; Remotely in the USA · Senior

Sponsorship not specified$160k-$210kDetected 75 days ago
TypeScriptPythonNode.jsMySQLMongoDBRedisVector DatabasesAzureCloud PlatformsDockerKubernetesTerraformDevOpsKafkaData EngineeringLLMsAgentic AICybersecurityComplianceSAPCustomer Support

About the role

  • FDEs are our in-field product engineers.
  • What you ship not only makes deployments succeed, it also defines how customers experience Parloa and shapes what our product becomes.
  • This is about engineering what makes it work in production, under real-world enterprise constraints, at speed and scale

Responsibilities

  • Lead the technical execution (incl. product engineering) of Parloa's deployments inside large, complex enterprise environments
  • Build custom extensions, integrations, and configurations to close product gaps and meet enterprise requirements
  • Collaborate directly with customer engineering organizations to overcome constraints and deliver measurable outcomes
  • Collaborate with and provide technical guidance to more junior in-field engineers
  • When millions of people reach out to a brand, those interactions aren't just support tickets; they're defining experiences.
  • You will build what doesn't yet exist, adapt architecture to enterprise realities, and engineer integrations across systems and APIs to make deployments successful.
  • Own deployment engineering projects: Lead the technical execution (incl. product engineering) of Parloa's deployments inside large, complex enterprise environments
  • Design for scale and resilience: Architect deployment solutions that meet enterprise-grade requirements for performance, reliability, and security
  • Engineer solutions where none exist: Build custom extensions, integrations, and configurations to close product gaps and meet enterprise requirements
  • Partner with enterprise teams: Collaborate directly with customer engineering organizations to overcome constraints and deliver measurable outcomes

Requirements

  • 7+ years of professional experience in software engineering, systems integration, DevOps, or data engineering roles with direct customer impact
  • Proven track record of leading deep technical deployments and integrations in large-scale enterprise environments
  • Ability to operate independently in ambiguous, high-responsibility settings, especially when deploying live to enterprise systems
  • Degree in Computer Science, Engineering, or related technical field
  • We're at the beginning of a new era in customer experience, one where AI doesn't just respond, but understands, reasons, and takes action.

Skills

  • Operate at the intersection of backend engineering, DevOps, and data engineering to ensure seamless delivery
  • Work across systems & stacks: Operate at the intersection of backend engineering, DevOps, and data engineering to ensure seamless delivery

Compensation

  • $160,000 - $210,000 USD

Benefits

  • We provide equal opportunities to all qualified applicants regardless race, gender, sexual orientation, age, religion, national origin, disability status, socioeconomic background and other characteristics.

Company info

  • making every conversation seamless, intelligent, and genuinely helpful.
  • If you care about shaping how businesses and customers connect at scale-and want your work to matter in real, everyday moments-this is where you do it.
  • At Parloa, ownership isn't a buzzword; it means being accountable for outcomes, not just tasks.
  • We operate in a category that's evolving fast, where the bar is high, and the problems are complex.
  • We hire people who think in solutions, communicate with clarity, and follow through.
  • People who are comfortable making decisions, taking responsibility, and raising the standard for themselves and those around them.
  • Parloa's mission is to make every customer conversation feel effortless for both customers and the companies serving them.

This listing is sourced directly from Parloa's careers page and normalized into a canonical job model.