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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # AI Platform Test Lead - **Company:** TotalMed - **Location:** Alameda, CA, United States - **Experience:** Expert - **Salary:** $260,000.0 - $343,200.0 - **Contract:** Permanent contract - **Skills:** Artificial Intelligence, Amazon Web Services, Automation of Tests, Mobile Application Development, Software as a Service, Software Quality, Information Systems, Continuous Integration, Data Architecture, Graph Database, Machine Learning, Systems Development Life Cycle, Software Engineering, Tricentis Tosca, Enterprise Software Applications, Feature Engineering, Large Language Models, Multi-Agent Systems, Prompt Engineering, Model Validation, Multi-Cloud, Generative AI, HybridCloud, AI Platforms, Information Technology, Machine Learning Operations, Virtual Agents, Automation Anywhere, Devsecops, GXP, Katalon Studio, Databricks - **Published:** July 27, 2026 - **Apply:** https://www.indeed.com/viewjob?jk=9347281490bab8b2 ## About the Role * Bachelor's degree in Computer Science, Software Engineering, Information Systems, or a related technical discipline (or equivalent experience). * 5+ years of experience in Software Quality Engineering, Test Automation, or AI/ML Engineering. * 3+ years of hands-on experience designing or supporting AI testing platforms, preferably within AWS environments. * Experience with one or more AI-enabled testing platforms such as: * Tricentis Tosca * Testim * ACCELQ * Mabl * LambdaTest * Katalon * Experience working with enterprise large language models (LLMs) such as Claude, OpenAI, or similar platforms. * Strong understanding of modern LLM concepts including: * Retrieval-Augmented Generation (RAG) * Fine-tuning * Embeddings * Vector databases * Graph databases * Multi-agent orchestration, * Experience supporting regulated environments with GxP validation processes for AI/ML systems. * Exposure to AI development frameworks such as: * LangChain * Semantic Kernel * Similar agent orchestration frameworks * Experience with AI testing, evaluation, and model validation tooling. * Knowledge of multi-cloud and hybrid cloud architectures. Key Competencies * Strategic mindset with strong hands-on technical execution. * Ability to evaluate emerging AI technologies and translate them into scalable business solutions. * Strong systems thinking across platforms, data architecture, AI workflows, and software engineering. * Excellent communication and stakeholder management skills with the ability to influence both technical teams and senior leadership. * Highly collaborative with a bias toward experimentation, continuous learning, and iterative delivery. * Passion for advancing software quality through AI-enabled automation and intelligent engineering practices., * Bachelor's (Required), * Software Quality, Test Automation or AI/ML Engineering: 5 years (Required) * AI Testing Platform (design or support): 3 years (Required) * AWS environment: 2 years (Preferred) ## Description We are seeking a forward-thinking AI Platform Test Lead to design, build, and operationalize AI-driven Quality Engineering (QE) testing platforms that accelerate innovation across the Software Development Lifecycle (SDLC). This role will lead the strategy, implementation, and ongoing operation of AI-powered testing capabilities while enabling scalable, secure, and compliant adoption of AI technologies across enterprise software environments. The ideal candidate will combine deep technical expertise in AI platforms and software quality engineering with the ability to evaluate emerging technologies and rapidly operationalize high-value capabilities within a regulated environment. Key ResponsibilitiesAI Quality Engineering Platform Strategy & Architecture * Define and evolve the enterprise AI QE testing platform architecture across cloud and data ecosystems. * Design interoperable AI-driven testing solutions supporting both AI-enabled and traditional enterprise software applications. * Build and support AI testing capabilities across: * AWS AI services (Bedrock, SageMaker, model hosting, orchestration) * Databricks / Mosaic AI (ML lifecycle, feature engineering, LLMOps) * Enterprise LLM platforms such as Claude * SaaS applications and internally developed software AI Capability Engineering & Operations * Design and operationalize reusable AI testing capabilities including: * Prompt engineering, tool orchestration, and AI agent frameworks * AI evaluation, monitoring, observability, and performance measurement * Secure and compliant AI usage aligned with regulated industry requirements * Implement AI governance, guardrails, auditability, and traceability across testing platforms. * Design, develop, and maintain AI-driven defect analytics and quality metrics. * Continuously evaluate emerging AI technologies and integrate high-value capabilities into enterprise testing practices. AI-Driven Software Development Lifecycle * Drive adoption of AI-enabled and agentic Software Development Lifecycle (SDLC) practices. * Define frameworks for: * Specification-driven AI-assisted development * AI coding assistants and autonomous development agents * Intelligent workflow automation across engineering processes * Integrate AI-native development platforms into CI/CD, DevSecOps, and enterprise engineering workflows. ## Related Videos - [AI as a Test Designer: Transforming Experience into Automated Testing](https://www.wearedevelopers.com/videos/1984-ai-as-a-test-designer-transforming-experience-into-automated-testing) - [Cutting LLM Costs Without Cutting Quality: How to Beat Proprietary LLMs with Fine-Tuned Open Source](https://www.wearedevelopers.com/videos/100151-cutting-llm-costs-without-cutting-quality-how-to-beat-proprietary-llms-with-fine-tuned-open-source) - [DevSecOps: Injecting Security into Mobile CI/CD Pipelines](https://www.wearedevelopers.com/videos/273-devsecops-injecting-security-into-mobile-ci-cd-pipelines) - [Blueprints for Success: Steering a Global Data & AI Architecture](https://www.wearedevelopers.com/videos/1577-blueprints-for-success-steering-a-global-data-ai-architecture) - [AI-Augmented DevOps with Platform Engineering](https://www.wearedevelopers.com/videos/1614-ai-augmented-devops-with-platform-engineering) - [From A2A to MCP: How AI’s “Brains” are Connecting to “Arms and Legs”](https://www.wearedevelopers.com/videos/1631-from-a2a-to-mcp-how-ai-s-brains-are-connecting-to-arms-and-legs) ## Related Articles - [Got AI ideas but no money? Here are 10 free ways to level up your AI skills with Google Cloud](https://www.wearedevelopers.com/magazine/600-got-ai-ideas-but-no-money-here-are-10-free-ways-to-level-up-your-ai-skills-with-google-cloud) - [How to Become an AI Engineer](https://www.wearedevelopers.com/magazine/331-how-to-become-an-ai-engineer) - [Navigating the AI Shift](https://www.wearedevelopers.com/magazine/629-navigating-the-ai-shift) - [From Prototype to Production: Build AI Agents with This Free 4-Course Learning Path](https://www.wearedevelopers.com/magazine/655-from-prototype-to-production-build-ai-agents-with-this-free-4-course-learning-path) - [Coffee with Developers - Maria Apazoglou - Making AI understandable for all in production](https://www.wearedevelopers.com/magazine/475-coffee-with-developers-maria-apazoglou-making-ai-understandable-for-all-in-production) - [MLOps And AI Driven Development](https://www.wearedevelopers.com/magazine/82-mlops-and-ai-driven-development)