Staff Forward Deployed Engineer

Colossus Technologies Group; Berkeley (CA); On-site; USD 130,000 - 170,000; Full time

Job summary

A rapidly expanding AI company is seeking a Staff Forward Deployed Engineer to lead the design and deployment of customer-facing AI agent systems. You will collaborate with clients to implement efficient workflows across complex environments. Ideal candidates have 7–10+ years of software engineering experience and strong communication skills. This role emphasizes the deployment of reliable AI systems and improving core platform capabilities, aiming for rapid, repeatable success in production environments.

Qualifications

• 7–10+ years of experience in distributed systems or platform engineering.
• Strong systems thinking across APIs and backend services.
• Experience in customer-facing software systems.

Responsibilities

• Lead technical delivery of AI agent deployments.
• Partner with customers to understand workflows.
• Design production-grade agent systems and deployment architecture.

Skills

Software engineering experience

Systems thinking

Debugging ability

Technical communication

Job description

A rapidly expanding Agentic AI company specializing in Voice AI and AI Automation is building intelligent agents designed to automate manual workflows across the $2.7T insurance industry. The platform initially focused on voice — one of the most complex and high-value channels in insurance — and is now expanding into full workflow automation across sales, servicing, and claims operations.

The long-term vision is to build reasoning agents capable of handling the full spectrum of insurance carrier and broker workflows.

Mission

As a Staff Forward Deployed Engineer, you will lead the design and deployment of customer-facing AI agent systems. You will work directly with customers and internal product teams to turn complex workflows into production‑grade agent deployments quickly, safely, and repeatably, while feeding improvements back into the core platform.

What You Will Do

• Lead the technical delivery of AI agent deployments across complex customer environments.
• Partner directly with customers to understand workflows, technical constraints, and success metrics.
• Design and implement production‑grade agent systems integrating APIs, orchestration layers, and automation pipelines.
• Define deployment architecture including evaluation frameworks, reliability standards, and observability practices.
• Translate recurring deployment patterns into reusable product capabilities and platform primitives.
• Collaborate with product and platform engineering teams to improve the underlying agent infrastructure.
• Debug complex production issues and establish best practices for reliability, safety patterns, and scaling agent workflows.
• Act as a technical leader across deployments while helping define standards and repeatable implementation patterns.

What We Are Looking For

• 7–10+ years of software engineering experience in distributed systems or platform engineering environments.
• Strong systems thinking across APIs, backend services, and production reliability.
• Experience building or integrating complex software systems in customer‑facing environments.
• Comfort working with emerging AI agent frameworks, orchestration systems, and evaluation pipelines.
• Strong debugging ability across distributed systems and production environments.
• Ability to translate ambiguous customer problems into structured technical solutions.
• Excellent communication skills and the ability to operate at the intersection of engineering and customer success.

Success in This Role

• Rapid deployment of reliable agent systems into production environments.
• Reusable deployment patterns that reduce one‑off implementations.
• Improved reliability, evaluation coverage, and observability across agent deployments.
• Strong feedback loops between customer deployments and core platform development.

What You Will Own

• Technical architecture and delivery of forward deployed AI agent systems.
• Deployment methodology including discovery, scoping, rollout validation, and post‑launch improvements.
• Engineering quality standards across agent reliability, evaluation frameworks, and observability.
• Creation of repeatable patterns that improve deployment speed and platform leverage over time.