Forward Deployment Engineer

Qualis1 Inc.; United States; On-site; USD 140,000 - 200,000; Full time

Job summary

Qualis1 Inc. is seeking a Forward Deployed Engineer to lead complex Conversational AI implementations at customer sites in the United States.

You will transform rapid prototypes into scalable, secure AI systems, owning end-to-end delivery and architecture in collaboration with customer engineers. You will drive customer success through multi-agent integration, robust eval and observability pipelines, and alignment with enterprise IT infrastructure.

Qualifications

• Bachelor’s degree in Engineering or Computer Science or equivalent practical experience.
• 5 years of experience with software development using Python or similar languages.
• Experience architecting AI systems on cloud platforms (e.g., GCP).
• Experience deploying resources via Terraform or similar tools to automate the setup of agents, functions, or networking.
• Experience building full-stack applications that interact with enterprise IT infrastructures and developing external customer projects.

Responsibilities

• Serve as the lead developer for complex Conversational AI and CX applications, transitioning from rapid prototypes to production-grade workflows.
• Architect and code conversational flows optimized for connectivity between Google’s Conversational AI products and customers’ live infrastructure, including APIs and data silos.
• Build high-performance Eval pipelines and observability frameworks to optimize agentic workloads with secure production-grade networking.
• Identify repeatable field patterns and friction points in Google’s AAI stack and translate them into reusable modules or feature requests for Engineering teams.
• Co-build with customer engineering teams to instill Google-grade development practices ensuring long-term project success and adoption.

Skills

Python

Terraform

GCP

AI architecture

Education

Bachelor’s degree in Engineering/CS

Tools

APIs

Cloud infrastructure

Job description

As a Forward Deployed Engineer (FDE) in Applied AI, you are the "Agent Engineer" and the primary driver for our customers' most critical AI initiatives.

You take initial conversational prototypes and transform them into production-ready solutions, owning the end-to-end engineering lifecycle, including the transition from "Art of the Possible" to real-world business value and scalable, secure AI systems.

This is a high-travel, high-impact role focused on leading technical delivery for Conversational AI pilots and establishing the first Customer User Journeys (CUJs) for our largest customers at their sites.

This role requires a deep understanding of software engineering, Machine Learning Operations, and cloud infrastructure.

Job responsibilities

Serve as the lead developer for complex Conversational AI and CX applications, transitioning from rapid prototypes to production-grade agentic workflows (e.g., multi-agent systems, MCP servers) that drive measurable ROI.

Architect and code conversational flows that are not just functional, but optimized for the "connective tissue" between Google’s Conversational AI products and customers’ live infrastructure, including APIs, legacy data silos, and security perimeters.

Build high-performance evaluation (Eval) pipelines and observability frameworks to optimize complex agentic workloads, focusing on reasoning loops, tool selection, and reducing latency while maintaining production-grade security and networking.

Identify repeatable field patterns and technical "friction points" in Google’s AAI stack, converting them into reusable modules or product feature requests for Engineering teams.

Co-build with customer engineering teams to instill Google-grade development best practices, ensuring long-term project success and high end-user adoption.

Bachelor’s degree in Engineering, Computer Science, a related field, or equivalent practical experience.

5 years of experience with software development using Python or similar coding languages.

Experience architecting AI systems on cloud platforms (e.g., GCP).

Experience deploying resources via Terraform or similar tools to automate the setup of agents, functions, or networking.

Experience building full-stack applications that interact with enterprise IT infrastructures and developing external customer projects.

Preferred qualifications

Master’s or PhD in AI, Computer Science, or a related technical field.

Experience implementing multi-agent systems using frameworks like ReAct and self-reflection.

Experience debugging Agent logic and optimizing tool selection, including tracing conversation IDs across microservices to identify and resolve failures in real-time.

Experience connecting agents to enterprise knowledge bases and optimizing RAG chunking to prevent hallucinations.

Track record of troubleshooting live, high-traffic systems during critical windows.