Forward Deployment Engineers

hands-on implementation of our platform in high-complexity, real-world customer environments - especially where product maturity gaps require deep technical adaptation. FDEs bridge the last mile between product vision and working production systems.

This is not a typical backend role. FDEs are trusted technical partners embedded in customer deployments. You will operate at the intersection of platform, integration, and delivery - building what doesn’t yet exist, adapting our architecture to enterprise constraints, and enabling multimillion-dollar AI transformation at global scale.

This role is ideal for engineers who are outcome-oriented, comfortable in ambiguity, and motivated by solving novel technical problems under pressure. It’s a high-autonomy, high-impact position where your work will directly shape how the world’s most complex enterprises interact with AI.

What You’ll Do

• Deploy and integrate Parloa's platform into complex enterprise environments, ensuring successful implementation and real-world impact
• Own system configurations, API integrations, custom code extensions, and performance tuning in customer-specific setups
• Act as a trusted technical partner to customer engineering teams, working collaboratively through ambiguity and unique constraints
• Solve tough problems in real-time; debug, unblock, and optimize field implementations at speed
• Partner with Deployment Strategists and Product teams to ensure feedback loops from the field inform broader platform improvements

What We’re Looking For

• 4+ years of experience as a software, implementation, or systems integration engineer in a customer-facing capacity
• Hands-on experience with Python or TypeScript, Terraform, Kubernetes, and cloud platforms (preferably Azure)
• Strong working knowledge of APIs, MongoDB/PostgreSQL/CosmosDB, CI/CD pipelines, and observability tools like Grafana or Prometheus
• Comfort working independently in fast-paced, field-facing roles with high levels of ambiguity and responsibility
• Track record of delivering successful implementations for large enterprise clients
• Degree in Computer Science, Engineering, or related technical field
• Certifications in cloud or infrastructure technologies (AWS, Azure, GCP, Kubernetes) are a strong plus
• Demonstrated hands-on experience developing or deploying AI solutions using LLMs, including crafting and optimizing prompts for specific use cases, and familiarity with designing or integrating agentic architectures for autonomous task workflows