Senior Software Engineer, GenAI

Tricentis•Austin, TX

3d•Hybrid

About The Position

We are looking for a Senior 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.

Requirements

4+ 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 (GitHub 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)
Proven track record of delivering systems that balance innovation with reliability and maintainability.
Strong expertise in software engineering best practices, including: Architecture patterns and distributed systems
CI/CD and DevOps integration (especially in continuous testing contexts)
Automated testing strategies and quality engineering principles
Experience building and scaling data pipelines and AI-powered platforms.
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.
Strong collaborator across engineering, product, and customer success teams.

Nice To Haves

Familiarity with enterprise application ecosystems (e.g., SAP, Oracle, Salesforce) is a plus.
Familiarity of: Vector databases and retrieval-augmented generation (RAG)
AI evaluation and observability frameworks
Test management platforms (e.g., Jira integrations)
Background working on enterprise SaaS platforms.
Exposure to AI-driven DevOps or continuous testing ecosystems.

Responsibilities

Contribute to AI-focused engineering initiatives, delivering high-quality implementations aligned with team direction using Spec driven development.
Participate in architecture and development of AI-driven testing capabilities.
Share knowledge through code reviews, design discussions, and team collaboration.
Make sound engineering decisions balancing performance, cost, scalability, and customer value.
Collaborate with Product Management and Design to translate customer needs into AI-powered features.
Contribute to internal knowledge sharing around AI.
Design and implement LLM-powered solutions for the next generation of software quality solutions.
Participate in experimentation efforts to validate new AI-driven testing approaches.
Contribute to testing strategies for AI systems, including: Model evaluation and benchmarking, Regression detection for AI behaviors, and Reliability and explainability validation
Ensure high-quality implementation through code reviews and adherence to best practices.
Partner with domain experts in enterprise ecosystems (e.g., SAP) to optimize testing solutions for complex business applications.
Contribute to improvements in DevOps integration and continuous testing pipelines.