Sr. AI QA Engineer / Lead

Roles and Responsibilities

• Quality Engineering Architecture & Workflow Optimization Lead architecture and technical implementation of AI Quality Engineering solutions supporting AI-powered applications, LLM-enabled workflows, intelligent automation solutions, agentic systems, and enterprise AI platforms.
• Design and implement scalable AI validation frameworks, AI-assisted testing approaches, runtime quality controls, reusable testing accelerators, and workflow optimization capabilities supporting enterprise AI delivery initiatives.
• Support modernization of traditional Quality Engineering practices through intelligent automation, workflow orchestration, and scalable quality engineering patterns.
• Develop automated validation approaches, anomaly detection processes, testing pipelines, and quality metrics supporting governance-aligned AI deployment practices.
• Design and optimize human-in-the-loop validation workflows, operational review processes, and AI quality assurance controls supporting reliable and scalable AI-enabled systems.
• Partner with engineering and business teams to identify operational bottlenecks, workflow optimization opportunities, automation use cases, and scalable quality engineering improvements.

Experience

• 8+ Years

Educational Qualifications

• Engineering Degree BE/ME/BTech/MTech/BSc/MSc.
• Technical certification in multiple technologies is desirable.

Mandatory Skills

• AI Validation
• Runtime Assurance & Automation
• Support AI validation activities, including prompt testing, workflow testing, regression testing, runtime quality assurance, and production reliability support.
• Partner with AI Engineering, AIOps, LLMOps, Security, Governance, Clinical, and Data teams to support scalable AI Quality Engineering and workflow automation processes across enterprise AI initiatives.
• Design and support runtime quality practices, including telemetry alignment, monitoring coordination, validation processes, and runtime reliability improvement efforts.
• Drive adoption of AI-assisted testing approaches, intelligent automation, reusable testing accelerators, and orchestration-aware testing practices.
• Support observability and runtime visibility initiatives improving reliability, traceability, and confidence across AI-enabled systems.
• Collaborate with Clinical, Operational, and Engineering stakeholders to support validation of healthcare workflows, operational processes, and AI-enabled business solutions.

Technical Enablement, Delivery & Operational Support

• delivery coordination activities across AI Quality Engineering and workflow optimization initiatives, including implementation planning, issue tracking, operational support, and release coordination activities.
• Partner with stakeholders to evaluate implementation readiness, workflow dependencies, operational risks, automation opportunities, and quality considerations for AI initiatives.
• Support tooling evaluations, automation frameworks, orchestration tooling, and modernization initiatives supporting AI Quality Engineering maturity.
• Help establish reusable workflow automation patterns, scalable testing assets, and engineering enablement practices across delivery teams.
• Support adoption of modern AI Quality Engineering and workflow optimization practices across engineering and business organizations.

Leadership, Collaboration & Continuous Improvement

• Lead and mentor engineers, analysts, contractors, and delivery teams while fostering a collaborative, continuously learning, and engineering-focused culture.
• Communicate implementation risks, workflow optimization opportunities, technical trade-offs, and operational recommendations to technical and business stakeholders.
• Promote engineering discipline, continuous improvement, responsible AI adoption, and operational accountability across AI Quality Engineering initiatives.