Lead AI Software Engineer

Lead AI Software Engineer – Austin, Texas (Hybrid)

Tricentis is a global leader in AI-driven, codeless software testing and quality engineering, empowering enterprises to accelerate digital transformation while reducing risk. Trusted by over 3,000 customers—including organizations such as T-Mobile and Allianz—Tricentis delivers innovative solutions such as Tosca, qTest, Testim, and SAP Test Automation to enable continuous, intelligent testing at scale.

We are looking for a Lead AI Engineer to drive the next generation of AI-powered testing and quality engineering solutions across the Tricentis product portfolio. In this role, you will help shape how AI transforms software testing—from intelligent test generation and impact analysis to autonomous validation systems. You’ll act as both a technical leader and hands‑on builder, working across teams to deliver scalable AI systems embedded into products like Tosca, qTest, and Testim.

• Technical Leadership & Team Enablement: Serve as the technical lead for AI-focused engineering initiatives, setting direction and ensuring excellence across teams. Guide the architecture and development of AI-driven testing capabilities, including codeless automation and intelligent test generation. Mentor engineers through design reviews, coaching, and hands‑on technical leadership. Drive engineering decisions balancing innovation, performance, cost, scalability, and customer value. Collaborate with Product Management and Design to translate customer needs into AI-powered features.
  
  Lead internal knowledge sharing around AI in quality engineering, LLM applications, and automation frameworks. Identify team capability gaps and support hiring, onboarding, and skill development.
• AI Architecture, Implementation & Quality Design: Design and deploy LLM-powered solutions for use cases such as:
  • Natural language test creation (qTest-like capabilities)
  • Test impact analysis and intelligent prioritization
  • Autonomous test maintenance and self-healing automation
  Build scalable systems supporting model-based testing and codeless automation frameworks. Lead experimentation efforts to rapidly validate new AI-driven testing approaches. Define and standardize testing strategies for AI systems, including:
  • Model evaluation and benchmarking
  • Regression detection for AI behaviors
  • Reliability and explainability validation
  Ensure high‑quality implementation through code reviews, architectural alignment, and best practices.
• Domain Collaboration & Dev Ops Integration: Partner with domain experts in enterprise ecosystems (e.g., SAP) to optimize testing solutions for complex business applications. Drive improvements in Dev Ops integration and continuous testing pipelines.

• 6+ years of demonstrated work experience building production-grade software using Python.
• 3+ years of experience designing and deploying AI/ML or LLM-based solutions in production.
• Hands‑on experience with AI coding assistants (Git Hub Copilot, Cursor, Claude Code, etc.).
• Strong knowledge of Generative AI and LLM ecosystems, agent-based systems (A2A) and orchestration frameworks, emerging standards such as Model Context Protocol (MCP), and a proven track record of delivering systems that balance innovation with reliability and maintainability.
• Deep expertise in software engineering best practices, including architecture patterns and distributed systems, CI/CD and Dev Ops integration (especially in continuous testing contexts), automated testing strategies and quality engineering principles, building and scaling data pipelines and AI-powered platforms.
• Familiarity with enterprise application ecosystems (e.g., SAP, Oracle, Salesforce) is a plus.
• Strong understanding of security, privacy, and compliance in AI-driven systems.
• Experience with containers and orchestration (Docker, Kubernetes).
• Ability to troubleshoot complex AI production issues, especially in large-scale enterprise environments.
• Experience with cloud platforms (Azure preferred, AWS/GCP acceptable).
• Excellent communication skills with the ability to explain AI concepts to technical and non-technical stakeholders, act as the…