AI Test Lead
About the Role
Seeking a forward-thinking AI Platform Test Lead to design, build, and operationalize the QE testing leveraging AI tools and utilities to accelerate innovation in IT’s Software Development Lifecycle. This role will lead the design, implementation, and operations of AI QE testing platforms, tools, and utilities to support a multi-platform AI ecosystem, spanning AWS (Bedrock, SageMaker), Databricks (Mosaic AI), Claude for Enterprise, and emerging AI-native/agentic engineering tools and platforms. The AI QE test platforms will also be used with non-AI software products. A critical success factor is the ability to stay ahead of industry trends, quickly validate new technologies, and operationalize high-value capabilities in a regulated life sciences environment.
Responsibilities
- AI QE Test Platform Strategy & Architecture
- Help define and mature Exelixis’ enterprise AI QE Test platform architecture across cloud and data ecosystems
- Design interoperable AI-driven QE test solutions to support software solutions for:
- AWS AI stack (Bedrock, SageMaker, model hosting, orchestration)
- Databricks / Mosaic AI (ML lifecycle, feature engineering, LLM ops)
- Claude for Enterprise (secure conversational AI and enterprise workflows)
- SaaS and in-house developed software products
- AI Capability Engineering & Operations
- Operationalize reusable AI capabilities: prompt, tool, and agent orchestration frameworks
- Build evaluation, monitoring, and observability pipelines
- Enable secure, compliant AI usage (GxP, HIPAA where applicable)
- Implement AI platform guardrails, auditability, and traceability
- Design and operationalize defect statistics
- Agentic Software Development Lifecycle (SDLC)
- Drive adoption of agentic SDLC practices
- Define frameworks for:
- Spec-driven agentic development (Claude Code, GitHub Copilot, code agents)
- Autonomous/semi-autonomous agents across workflows
- Integrate AI-native platforms into enterprise engineering workflows (CI/CD, DevSecOps)
Requirements
- 5+ years in software quality engineering and testing, AI/ML engineering
- 3+ years hands-on experience with AI Test platforms (AWS preferred)
- Proven experience with:
- One or more AI QE Testing Platforms: Tricentis Testim/Tosca, ACCELQ, Mabl, LambdaTest, Katalon Enterprise
- LLM platforms (e.g., Claude, OpenAI, or similar)
- Strong understanding of LLM architectures (RAG, fine-tuning, embeddings, Vector DBs, Graph DBs, multi-agent orchestration)
Preferred Qualifications
- Familiarity with:
- GxP validation processes for AI/ML systems
- Agent frameworks (LangChain, Semantic Kernel, etc.)
- AI testing and evaluation tooling
- Multi-cloud/hybrid architectures
Skills
- Strategic + hands-on balance (thinks like an architect, executes like an engineer)
- Ability to translate emerging AI trends into enterprise value
- Strong systems thinking across platforms, data, and workflows
- Excellent stakeholder communication—able to influence senior leadership and engineering teams alike
- Bias for action—rapid experimentation and iterative delivery