This is a remote position.

We are seeking a
Quality Engineering & AI Validation Manager
to lead testing, validation, and quality strategy for AI-driven applications and data platforms. This role will be responsible for ensuring the reliability, accuracy, and performance of solutions built across
AWS, Azure, BI tools (Tableau, Power BI), and CRM platforms such as DealCloud
.

This individual will play a critical role in establishing
QA frameworks for AI/ML systems
, including model validation, data quality assurance, and end-to-end testing of integrated solutions.

Key Responsibilities

• 
  Lead the
  QA strategy and testing frameworks
  for AI-enabled applications and data platforms
• 
  Define and implement
  AI/ML validation methodologies
  , including model accuracy, bias detection, and performance testing
• 
  Oversee
  end-to-end testing
  across cloud environments (AWS, Azure), data pipelines, BI tools, and CRM integrations
• 
  Establish
  automated testing frameworks
  for APIs, data workflows, and application layers
• 
  Develop and enforce
  data quality validation processes
  , including completeness, consistency, and lineage checks
• 
  Partner with data, engineering, and architecture teams to ensure
  testability and quality by design
• 
  Validate integrations across systems including
  Tableau, Power BI, and DealCloud CRM
• 
  Drive
  performance, load, and scalability testing
  for high-volume data and AI workloads
• 
  Implement
  CI/CD quality gates
  and integrate testing into DevOps pipelines
• 
  Lead and mentor QA engineers and automation testers
• 
  Ensure compliance with
  security, regulatory, and governance standards

Requirements

​

Required Qualifications

• 
  7+ years of experience in
  Quality Engineering, QA, or Test Management
  roles
• 
  Experience leading QA efforts in
  cloud-based environments (AWS and/or Azure)
• 
  Strong background in
  test automation frameworks
  (e.g., Selenium, Cypress, Playwright, or similar)
• 
  Experience testing
  data pipelines, ETL processes, and data platforms
• 
  Understanding of
  AI/ML validation concepts
  , including model testing, data drift, and bias detection
• 
  Experience with
  API testing and microservices architectures
• 
  Familiarity with
  BI tools such as Tableau and Power BI
• 
  Strong experience with
  SQL and data validation techniques
• 
  Experience integrating QA into
  CI/CD pipelines (DevOps practices)

Key Traits for Success

• 
  Ability to build QA processes in a
  greenfield or evolving AI environment
• 
  Strong leadership and ability to influence cross-functional teams
• 
  Detail-oriented with a focus on
  data integrity and system reliability
• 
  Strategic thinker who can balance
  speed and quality in AI development