About Us
Sauce Labs is the world’s largest full-lifecycle, test automation platform, and the company behind Selenium. Trusted by 80% of the world’s top ten largest financial institutions and over 300,000 enterprise users, Sauce Labs provides the only AI platform capable of turning business intent into autonomous testing and quality assurance. With a proprietary dataset of 8.7 billion test runs, Sauce Labs empowers the Fortune 2000 to bridge the gap between AI-driven code generation and enterprise-grade software quality. Learn more at saucelabs.com.
The Role
We are seeking an innovative and experienced AI Architect to join our engineering leadership team. This is a strategic role that will be instrumental in designing and building the next generation of AI-powered features for our continuous testing platform. You will be responsible for architecting scalable and robust AI solutions that transform how our customers gain insights from their test data and production environments, and how they create tests.
Responsibilities
- Define AI Architecture: Lead the design and architecture of cutting-edge AI/ML solutions for new product offerings, ensuring scalability, performance, quality and reliability within a cloud-native environment.
- AI-Powered Insights (Test & Production): Architect AI systems to derive actionable insights from vast quantities of test run logs and analytics data. This includes identifying patterns, anomalies, and performance trends.
- Production Error Reporting Integration: Design AI solutions that integrate with our existing error reporting product to analyze production issues for mobile and web applications, providing deeper understanding and predictive capabilities.
- Unified Data Intelligence: Develop architectures for combining insights from both test runs and production data, creating a holistic view of application quality and user experience.
- Automated Failure Analysis & Remediation: Architect AI models and systems that can automatically identify failures in test runs, diagnose root causes, and suggest potential fixes or optimizations.
- AI-Assisted Test Case Generation: Design AI-driven capabilities to assist in the intelligent generation of test cases, improving test coverage and efficiency.
- Leverage Cloud AI Platforms: Work extensively with Google Cloud Platform (GCP) services, specifically focusing on leveraging and integrating with our self-hosted versions of Gemini Flash models for text generation and understanding.
- Data Strategy & Pipeline: Collaborate with data engineering teams to define and optimize data pipelines, ensuring efficient data flow from sources (Snowflake, with an eye towards Google BigQuery) to AI models.
- Enhance Delivery Process: Help identify the areas of opportunity for delivery process improvements through AI tools and validate the feasibility.
- Technical Leadership: Provide technical leadership and mentorship to engineering teams, guiding them in the implementation and deployment of AI/ML solutions. Take part in AI governance projects.
- Research & Innovation: Stay abreast of the latest advancements in AI/ML, evaluating new technologies and methodologies to continuously enhance our product offerings.
- Cross-Functional Collaboration: Work closely with product management, engineering teams, and senior leadership to translate business requirements into technical AI solutions and roadmaps.
Required Skills
Education & Experience:
- Bachelor’s or Master’s degree in Computer Science, Engineering, or a related field.
- 7+ years of experience in software development and architecture of production-grade systems.
- Extensive AI/ML Architecture Experience: Proven experience in designing, building, and deploying large-scale AI/ML systems and platforms in a production environment.
- Cloud Expertise (GCP Preferred): Deep hands-on experience with Google Cloud Platform (GCP) services relevant to AI/ML (e.g., Vertex AI, BigQuery, Cloud Storage, Dataflow).
- Generative AI Experience: Practical experience with large language models (LLMs), particularly with models like Gemini Flash, and understanding of their deployment and fine-tuning (especially with regard to prompt engineering, agent orchestration, context windows management and output validation).
- Big Data & Analytics: Strong background in working with large datasets, data warehousing (Snowflake, BigQuery), and analytics platforms.
- Software Engineering Foundation: Solid understanding of software development best practices, scalable system design, and microservices architectures.
- Problem-Solving & Innovation: Exceptional analytical skills with a track record of solving complex, ambiguous problems using AI/ML techniques.
- Leadership & Communication: Excellent communication and presentation skills, with the ability to articulate complex technical concepts to both technical and non-technical audiences, including senior leadership.
- Hands-on Capability: While an architectural role, a willingness to be hands-on with prototyping, proof-of-concepts, and guiding implementation.
- Domain Knowledge (Highly Desired): Familiarity with software testing, quality assurance, DevOps, or application performance monitoring.
Nice to Haves:
- Experience with MLOps practices and tools for managing the ML lifecycle.
- Contributions to open-source AI/ML projects or relevant publications.
- Advanced degree in Computer Science, AI, Machine Learning, or a related quantitative field.