Key Responsibilities
Architect and design agentic AI systems tailored for software testing phases including test planning, generation, execution, defect triaging, and reporting.
Lead the development of autonomous agents capable of reasoning, learning, and collaborating across the test life cycle.
Build and optimize AI pipelines using Google Cloud services (Vertex AI, BigQuery, Cloud Functions, etc.).
Integrate agents with CI/CD pipelines, test management tools, and developer environments.
Ensure seamless deployment and orchestration on AgentSpace, leveraging its capabilities for agent lifecycle management, communication, and scalability.
Collaborate with cross-functional teams including QA, DevOps, and product engineering to align AI capabilities with business goals.
Define and enforce best practices for agentic AI, including safety, interpretability, and performance monitoring.
Stay ahead of emerging trends in LLMs, multi-agent systems, and autonomous software engineering.
Required Qualifications
Proven experience as an AI Architect, ML Engineer, or similar role with a focus on agentic or autonomous systems.
Strong expertise in Google Cloud Platform and its AI ML ecosystem.
5+ years of experience in building AI/ML solutions, with at least 1+ year focused on generative AI.
Hands on experience with LLMs reinforcement learning and multi agent frameworks
Proficiency in Python and relevant AI libraries (e.g., TensorFlow, PyTorch, LangChain).
Familiarity with software testing tools (e.g., Selenium, TestNG, JUnit) and lifecycle processes.
Experience deploying and managing agents on AgentSpace or similar platforms.
Excellent problem solving, communication, and leadership skills
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