AI Quality Engineer

Role Overview We are seeking a skilled and hands-on AI Applications Developer to join our team as an onshore contractor. In this role, you will focus on developing cutting-edge AI-based platforms, agents, and applications. The ideal candidate will have a strong foundation in full-lifecycle software engineering, proven application development and deployment experience, and deep familiarity with AI-native development workflows. We are specifically looking for a mid-level professional (7 10 years of experience) who is comfortable driving the technical execution of AI integrations from end to end using the MERN stack and other modern technologies. A candidate with a Quality Engineering (QE) background is highly preferred to ensure robustness and reliability in our AI deployments. Key Responsibilities Application Development & Deployment: Apply proven, hands-on experience in building, testing, and deploying robust applications to production environments. AI Application Integration: Design, build, and deploy AI-based platforms, applications, agents, and skills. Vertex AI Integration: Lead AI model development and application design leveraging Google Vertex AI. RAG & EVALs Implementation: Architect and develop Retrieval-Augmented Generation (RAG) pipelines and build comprehensive EVALs frameworks to measure and ensure model performance and accuracy. API & Integration Architecture: Apply advanced knowledge to design and build scalable integration platforms, APIs, and web services. Full SDLC Execution: Manage the complete software development life cycle (SDLC) from initial design and build through comprehensive testing to final production deployment. Required Qualifications & Skills Core Tech Stack: Proven expertise in the MERN stack (MongoDB, Express.js, React, Node.js). Programming Languages: Solid technical knowledge of high-level programming languages including Python, TypeScript, JavaScript, and Java. AI-Native Development: Proven experience utilizing AI native development tools to accelerate workflows (e.g., Cursor, GitHub Copilot, Claude Code, and MCPs). Systems Architecture: Solid understanding of databases, application interfaces, and various application program development alternatives. Development Tools (5+ Years Experience): Proficient with modern IDEs (VS Code, JetBrains). Version control systems (Git, GitHub). Package management (npm/yarn, pip, maven/gradle). Containerization (Docker). Preferred Qualifications Quality Engineering (QE) Background: Strong preference will be given to candidates with previous experience in QE, demonstrating a rigorous approach to testing, validation, and system reliability within the SDLC.