Forward Deployed AI Engineer – System Test Automation

We are seeking a Forward Deployed AI Engineer (FDE) to accelerate the real-world adoption of AI within our End-to-End System Test Automation. This role focuses on taking AI tools and prototypes and making them work reliably inside complex engineering environments. You will embed closely with system test engineers, automation framework/ test owners and development teams to adapt, harden and operationalize AI-driven workflows. This is a hands-on engineering role for individuals who thrive in ambiguity, enjoy working close to real users and can turn AI potential into measurable productivity gains. Key Responsibilities: Forward Deployment of AI into Test Automation and turn AI prototypes into production-ready, repeatable solutions Embed with system test and automation teams to: Understand real testing workflows, constraints, and failure modes Identify where AI can safely and effectively reduce manual effort Adapt AI workflows to work with: Existing automation frameworks Real test data, schemas, and configurations Agentic AI Implementation & Tuning Implement and customize agentic AI workflows that: Interpret requirements, schemas, or models Assist in generating structured test assets Tune AI behavior based on: Real test outcomes Failure analysis and feedback Debug and resolve AI issues in live engineering environments AI Tooling & Platform Feedback Work with AI platforms (e.g., Qodo or similar) to: Extend functionality where needed Configure prompts, workflows, and validation layers Evaluate emerging AI tools and frameworks in real system test contexts Feed practical insights back to platform and leadership teams: What works What fails What should scale Enablement, Documentation & Guardrails Document AI usage patterns and best practices Help define guardrails for: Safety-critical configurations Invalid or destructive AI-generated outputs Enable test engineers to adopt AI confidently and responsibly Required Qualifications • 2+ years of hands-on experience in software engineering or applied AI, with clear ownership of deploying solutions into real engineering or production environments • Demonstrated experience taking an AI or automation-based solution from prototype to real-world usage, including adapting it to real constraints, failures, and evolving requirements • Proven ability to own and troubleshoot AI behavior, tuning prompts/instructions, adding validation or guardrails, and diagnosing incorrect, unsafe, or low-quality outputs • Experience working closely with engineers or end users to understand real workflows and iterating rapidly based on live feedback and outcomes • Strong hands-on programming skills (Python required), with experience building orchestration, integration, or control logic across multiple systems Preferred Qualifications • Networking fundamentals (TCP/IP, MPLS, gNMI, YANG) • Linux, CI/CD, Docker, Kubernetes