What are the responsibilities and job description for the AI Test Lead position at Planet Pharma?
MARKET RATE
Role Overview
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. This role will ensure scalable, secure, and compliant deployment of AI capabilities while enabling rapid experimentation and adoption.
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.
Key Responsibilities
Role Overview
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. This role will ensure scalable, secure, and compliant deployment of AI capabilities while enabling rapid experimentation and adoption.
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.
Key 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 to support
- 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
- 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
- Drive adoption of agentic software development lifecycle (SDLC) practices
- Define frameworks for:
- Spec-driven agentic development (Claude Code, Github Copilot, code agents)
- Autonomous/semiautonomous agents across workflows
- Integrate AI-native platforms into enterprise engineering workflows (CI/CD, DevSecOps)
- 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:
- 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)
- Familiarity with:
- GxP validation processes for AI/ML systems
- Exposure to:
- Agent frameworks (LangChain, Semantic Kernel, etc.)
- AI testimg and evaluation tooling
- Multi-cloud / hybrid architectures
- 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