Software Engineering SMTS (Salesforce AI Quality Engineer)

PRIMARY RESPONSIBILITIES

• Quality Engineering Strategy Leadership: Define, develop, and execute a comprehensive quality engineering and testing strategy specifically for Salesforce Agentforce, Data Cloud, and other relevant Salesforce platform capabilities.
• Test Automation Expertise: Design, develop, and maintain robust automated test scripts for Agent UI, Experience Site UI, and APIs using industry-standard tools and frameworks such as Selenium, WebDriver, JavaScript frameworks, and API testing tools.
• AI Agentforce Testing: Develop specialized testing strategies and test cases for AI-powered features, including Retrieval-Augmented Generation (RAG), Agentic workflows, single turn / multi Turn conversation, Natural Language Processing (NLP), and Large Language Models (LLMs). Use AI and RAG testing metrics to define and measure testing outcome.
• Data-Driven Quality: Build and maintain datasets for training, fine-tuning, and evaluating the performance and accuracy of LLMs and other AI models. Implement data-driven approaches to measure and improve the quality of AI features.
• Functional Salesforce Testing: Design and execute functional test plans and test cases tailored to Salesforce applications, including customizations using Flows, Triggers, Apex Jobs, and more. Possess a strong understanding of Salesforce best practices and governor limits.
• Continuous Integration Continuous Delivery (CI/CD): Build and maintain automated quality control pipelines using Jenkins or other CI/CD tools to ensure seamless integration and deployment of high-quality software.
• Defect Management: Effectively identify, document, track, and manage defects to ensure timely resolution and contribute to root cause analysis.
• Collaboration Communication: Partner effectively with Product Managers, Developers, UX Designers, and other stakeholders to understand requirements, provide timely feedback, communicate testing progress, and collaborate on solutions.
• Best Practices Standards: Establish and champion quality standards, methodologies, and best practices within the team and across the organization.
• Performance Optimization: Continuously assess the performance of Agentforce implementations , identify potential bottlenecks, and recommend and implement proactive measures for optimization.
• Technical Expertise: Stay up-to-date with the latest Salesforce releases, AI advancements, and emerging testing trends.
• Stakeholder Management: Communicate testing progress, risks, and mitigation strategies effectively to various stakeholders, including both technical and non-technical audiences.
• Agile Participation: Actively participate in Agile development methodologies, contributing to sprint planning, daily stand-ups, and retrospectives.
• Problem-Solving Analytical Skills: Demonstrate strong analytical and problem-solving skills to identify and resolve complex quality issues effectively.

Professional Experience/Skills Required:

• Overall 7+ years of experience in Software Quality Engineering.
• 2+ years of hands-on experience with the Salesforce platform, including configuration and development across various Salesforce Customer 360 products (e.g., Sales Cloud, Service Cloud, Data Cloud, Einstein ).
• 3+ years of proven experience in testing Machine Learning (ML), Artificial Intelligence (AI) systems, and/or services.
• Strong proficiency in at least one programming language such as Python or Java, particularly relevant for test automation and AI/ML testing.
• Experience with Large Language Models (LLMs) and prompt engineering testing frameworks.
• Solid understanding of Retrieval-Augmented Generation (RAG) and Agentic technologies.
• Demonstrated experience in building and maintaining robust automated testing frameworks for UI and APIs.
• Proficiency in JavaScript and modern UI frameworks (e.g., React, Angular, Vue.js) for UI test automation.
• Experience with WebDriver and Selenium for browser automation.
• Strong analytical mindset with experience in using a data-driven approach to measure and improve the quality of complex ML/AI products.
• Proven experience with Salesforce DevOps, CI/CD processes, and automation tools.
• Deep understanding of Salesforce architecture, functionalities, and various clouds.
• Strong understanding of AI, machine learning, and natural language processing principles.
• Experience with data management principles and Customer Data Platforms (CDPs) or Data Lakes.
• Familiarity with development tools like SLDS, XML, HTML, SQL, JavaScript, JSON, and CSS.
• Proven ability to work effectively within cross-functional teams to achieve common goals and business objectives.
• Experience with Agile development or Scrum project management methodologies and tools.
• Solid understanding of software engineering principles, key design patterns, and best practices.
• Strategic thinker with the ability to see the big picture, innovate, and adapt to constant change.
• Ability to work under pressure, highly adaptable, and well-organized.
• Highly effective written and verbal communication skills with audiences across all levels of the organization.
• Strong organizational skills with the ability to establish and manage priorities in a complex and fast-paced environment.
• Strong knowledge of data management concepts and architectures.
• Bachelors or Masters degree in Computer Science, Software Engineering, or equivalent experience.

Salesforce Certifications (Preferred):

• Salesforce Platform App Builder
• Salesforce Administrator
• Salesforce-specific certifications related to Agentforce (if and when available)

Accommodations

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