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Parloa is an agentic AI platform that powers enterprise customer experience at scale, having processed over one billion customer interactions for brands like Booking.com, HealthEquity, Allianz, and SAP.
As a Forward Deployed Engineer, you will lead end-to-end deployment projects from an engineering perspective, operating on the front lines of enterprise AI adoption. This is a hands-on, code-first role where you engineer solutions directly in the field to make complex deployments succeed at enterprise scale.
Key responsibilities include:
- Own deployment engineering projects: Lead technical execution of Parloa deployments in large, complex enterprise environments
- Design for scale and resilience: Architect solutions meeting enterprise-grade requirements for performance, reliability, and security
- Engineer custom solutions: Build extensions, integrations, and configurations to close product gaps and meet enterprise requirements
- Work across systems and stacks: Operate at the intersection of backend engineering, DevOps, and data engineering
- Partner with enterprise teams: Collaborate directly with customer engineering organizations to overcome constraints
- Guide and mentor: Provide technical guidance to junior in-field engineers
- Influence at senior levels: Engage with enterprise architects and senior stakeholders on solutions and architectures
- Debug under pressure: Rapidly unblock issues in mission-critical environments
- Shape the product: Act as feedback loop between field and core engineering teams
- Deliver long-term impact: Work with Deployment Strategists on scalable architectures
Technology stack includes Python, TypeScript, Node.js, OpenAI, Microsoft Azure, Kubernetes, Docker, Terraform, MongoDB, MySQL, Redis, Kafka, and MCP.
Required: 4+ years in software engineering, systems integration, DevOps, or data engineering with direct customer impact; proven track record leading deep technical deployments in large-scale enterprise environments; strong technical depth in backend engineering, infrastructure-as-code, Kubernetes/cloud platforms, APIs, and databases; ability to operate independently in ambiguous, high-responsibility settings; builder's mindset with bias for action; degree in Computer Science, Engineering, or related field. Strong plus: experience building or integrating AI/LLM-powered systems.
