Quality Engineer - GenAI And Data Platforms

Optum is a global organization that delivers care, aided by technology to help millions of people live healthier lives. The work you do with our team will directly improve health outcomes by connecting people with the care, pharmacy benefits, data and resources they need to feel their best. Here, you will find a culture guided by inclusion, talented peers, comprehensive benefits and career development opportunities. Come make an impact on the communities we serve as you help us advance health optimization on a global scale. Join us to start Caring. Connecting. Growing together.

Role Overview

We are looking for a Quality Engineer to work on Generative AI-enabled web applications, built on .NET APIs and React frontend, with a solid data and analytics backbone. This role is critical in ensuring the quality, reliability, safety, and compliance of AI‑driven features, data pipelines, and analytical insights.

The engineer will actively test GenAI use cases, APIs, UI components, and data pipelines, and should be comfortable running and validating Databricks and Azure Data Factory (ADF) pipelines as part of end‑to‑end testing.

With every role in the company, each position has a requirement for building quality into every output. The Technology Quality Function is reserved for those positions where Quality is the primary objective of the work delivered. Technology Quality roles will cover all primary quality activity across all technology functions that ensure we deliver high quality products and services. These roles include, but are not limited to, strategy, planning, standards, process, policy, tools, engineering, assurance, management, metrics, research and development, automation, data preparation, maintenance, new development, operations and delivery.

Primary Responsibilities

• Analyzes and investigates
• Provides explanations and interpretations within area of expertise
• GenAI and Application Quality
• Validate Generative AI features such as chat interfaces, summarization, insights, and recommendations
• Perform prompt and response testing, including edge cases, ambiguity handling, and failure scenarios
• Test AI behavior for accuracy, relevance, consistency, and safety
• Identify and report issues related to hallucinations, incorrect reasoning, or incomplete responses.
• API and Backend Testing (.NET)
• Test .NET‑based REST APIs for functionality, validation, error handling, performance, and security
• Perform integration testing between APIs and GenAI services/models
• Validate payloads, response schemas, and downstream system interactions
• Support API automation as part of CI/CD pipelines
• Frontend Testing (React)
• Test React‑based web applications for:
• Functional correctness
• UI behavior and validations
• Cross‑browser and responsive behavior
• Validate AI‑driven UI components such as chat windows, dashboards, and analytics views
• Data and Analytics‑Centric Testing
• Validate data accuracy, completeness, and consistency across analytics dashboards and reports
• Test business logic and calculations used in analytical outputs
• Validate data used by AI models and retrieval systems
• Databricks and ADF Pipeline Validation
• Run and validate Databricks notebooks and jobs as part of test execution
• Trigger, monitor, and validate Azure Data Factory pipelines for both batch and incremental loads
• Verify data transformations, schema changes, and quality checks
• Perform end‑to‑end data flow validation from ingestion to consumption layers
• Automation and Quality Engineering
• Develop and execute automated tests for APIs, UI, and data validations where applicable
• Design reusable test utilities for AI and data validation
• Support regression testing for AI model, prompt, configuration, or pipeline changes
• Compliance and Responsible AI
• Validate applications for data privacy, PHI/PII handling, and access controls
• Support Responsible AI and governance checks, including fairness, bias, and safe usage validations
• Ensure auditability and traceability of test results and AI outputs
• Comply with the terms and conditions of the employment contract, company policies and procedures, and any and all directives (such as, but not limited to, transfer and/or re-assignment to different work locations, change in teams and/or work shifts, policies in regards to flexibility of work benefits and/or work environment, alternative work arrangements, and other decisions that may arise due to the changing business environment). The Company may adopt, vary or rescind these policies and directives in its absolute discretion and without any limitation (implied or otherwise) on its ability to do so
  

• Use GenAI tools (Copilot, Claude, Gemini AI) to ideate, prototype, and implement features efficiently
• Apply Vibe coding techniques to maintain clean, expressive, and AI-augmented codebases
• Develop internal tools and scripts using AI to automate repetitive tasks and improve productivity
• Stay current with emerging GenAI trends, tools, and ethical considerations
  
  

• Graduate degree or equivalent experience
• Bachelor's degree in Computer Science or a related engineering discipline (Information Technology, Software Engineering, Data Engineering, etc.)
• 2+ years of hands‑on experience as a Quality Engineer / Software Tester / QA Engineer
• Core QA Skills
• Solid experience in manual and automated testing
• Test planning, defect management, and root‑cause analysis experience
• Working experience in Agile / Scrum teams
• Backend & Frontend
• .NET / REST API testing (Postman, REST clients, or automation frameworks) experience
• React application testing experience
• Good understanding of web technologies (HTTP, JSON, browser behavior)
• Data & Cloud
• Experience with Azure Data Factory (ADF) pipelines
• Hands‑on knowledge of Databricks (running notebooks, validating outputs)
• Understanding of data warehousing / analytics concepts
• Solid SQL skills for data validation and analysis
• GenAI & AI Testing
• Understanding of Generative AI and LLM‑based systems
• Knowledge of:
• Prompt‑based interactions
• Non‑deterministic outputs
• RAG‑style or context‑aware AI workflows
• Demonstrated ability to evaluate AI quality beyond simple pass/fail outcomes
  

• Python or scripting skills for test automation experience
• Experience testing AI/ML or analytics platforms experience
• CI/CD tools and pipeline‑based testing experience
• Exposure to monitoring and production validation of AI systems
  
  

• Solid analytical and problem‑solving skills
• Ability to articulate AI quality risks clearly to engineering and product teams
• Willingness to work closely with developers, data engineers, and AI engineers
• High ownership mindset and attention to detail