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United States
Remote only
This position is listed on behalf of a partner company, who manages all applications and next steps. Our partner is looking for a Staff AI Engineer, Agentic Automation based in the United States.
Join an innovative security engineering team working to solve one of enterprise security’s most overlooked challenges: automating identity management across disconnected applications. As a Staff AI Engineer, you will lead the development and production deployment of intelligent AI agents that onboard applications, generate API specifications, and repair broken automations autonomously. You will build on existing agent systems to create reliable, self-healing workflows that adapt to changing application interfaces and APIs. This hands-on technical leadership role combines advanced LLM engineering, browser automation, distributed systems, and security-first architecture. You will establish evaluation frameworks, optimize model performance and operating costs, and define the future direction of agentic automation. Working closely with platform, integration, and runtime engineering teams, you will help deliver secure, scalable automation that reduces manual work and strengthens enterprise identity protection.
AI Agent Architecture and Production Ownership: Lead the end-to-end architecture, development, deployment, and ongoing operation of agentic application onboarding and self-healing automation systems. Define technical roadmaps, establish production-readiness standards, and take ownership of reliability, observability, and on-call responsibilities.
Intelligent Application Onboarding: Advance AI agents that analyze public API documentation through headless browsers and generate validated OpenAPI specifications. Develop agents that navigate SaaS application interfaces, execute workflows, and produce reliable, replayable automation sequences.
Self-Healing Automation: Design systems that detect changes in application interfaces, authentication flows, and API structures before they disrupt customer operations. Build agents that diagnose failures, repair workflows or specifications, and validate fixes against real executions before redeployment.
Evaluation and Quality Assurance: Establish and maintain benchmarks for generated API specifications, browser workflows, and automated repairs. Develop evaluation harnesses and measurable quality thresholds to determine when outputs are safe and reliable enough for production.
Performance and Cost Optimization: Monitor and optimize model accuracy, latency, and cost per generated output. Evaluate frontier and open-weight models using measurable performance data to balance quality, efficiency, and scalability.
Security and Risk Management: Define and implement security controls for AI agents interacting with sensitive credentials and untrusted websites. Address prompt injection, credential isolation, token management, sandboxed browsing, and network egress controls. Ensure secrets are excluded from prompts, logs, queues, and persisted workflow state.
Distributed Systems and Workflow Reliability: Productionize agent systems using durable workflow orchestration technologies, such as Temporal, and implement robust handling of retries, idempotency, asynchronous execution, and failures.
Cross-Functional Technical Leadership: Collaborate with integration, automation-runtime, and platform engineering teams to align architecture and implementation. Produce clear technical design documentation, guide engineering decisions, and mentor contributors as agentic automation capabilities mature.
Continuous Improvement and Strategic Direction: Drive the evolution of agentic automation toward increasingly autonomous application onboarding and recovery. Establish milestones for production adoption, reduced failure rates, lower operating costs, and minimizing the need for manual intervention.
Production AI Engineering Experience: Demonstrated experience shipping LLM-powered agents with tool-use capabilities that operate autonomously for real users, alongside ownership of backend systems in production.
Programming Skills: Strong proficiency in Python or TypeScript, with the ability to work productively in both languages.
LLM Agents and Tool Use: Deep understanding of agent loops, structured outputs, tool orchestration, retries, and common failure modes in autonomous AI systems.
Evaluation and Benchmarking: Experience building evaluation frameworks, benchmarks, or testing harnesses and using quantitative results to guide technical decisions and improve production quality.
Browser Automation and API Integrations: Significant experience with browser automation technologies such as Playwright or comparable tools, or deep expertise in backend and API integration systems, combined with the ability to work effectively across both areas.
Distributed Systems: Understanding of queues, idempotency, durable execution, and workflow orchestration platforms such as Temporal, AWS Step Functions, or similar technologies.
Security Expertise: Strong security judgment when designing systems that handle credentials and sensitive information, including familiarity with prompt injection risks, secret isolation, and secure execution environments.
Technical Leadership: Ability to take early-stage systems from development to production independently, make sound architectural trade-offs, and ensure systems remain reliable after launch.
Communication and Collaboration: Excellent technical writing skills, with the ability to document architecture, communicate complex decisions, influence stakeholders, and mentor engineers across teams.
Preferred Technical Experience: Hands-on experience with Temporal in production, Pydantic AI or comparable agent frameworks, open-weight model serving platforms such as Fireworks, Together, or Amazon Bedrock, and LLM cost modeling.
Additional Preferred Skills: Familiarity with computer-use agents, Chrome Manifest V3 browser extensions, OpenAPI tooling, identity and access management (IAM), credential management, AWS, and Terraform.
Remote work: Opportunity to work remotely from anywhere in the United States.
Technical ownership: Lead critical AI automation systems from early development through production and ongoing operation.
Innovation: Work on challenging problems at the intersection of agentic AI, enterprise security, browser automation, and identity management.
Technical influence: Shape architecture, engineering standards, evaluation frameworks, and the long-term direction of agentic automation.
Professional growth: Develop expertise in production-grade LLM systems, distributed workflows, self-healing automation, and AI security.
Collaborative environment: Partner with engineering specialists across integrations, platform development, and automation infrastructure.
Meaningful impact: Help enterprises reduce manual identity-management work, improve automation reliability, and strengthen security across disconnected applications.
