AI Quality Engineering Lead - Hybrid NYC

Expert Technology Services

Job Summary for AI Quality Engineering Lead

- Lead the enterprise adoption of AI-powered Quality Engineering solutions across the Software Development Lifecycle (SDLC) and Testing Center of Excellence (TCoE).
- Define and execute strategies, roadmaps, standards, and governance models for AI Quality Engineering.
- Design, implement, and scale enterprise AI solutions to improve software quality, engineering productivity, automation, and SDLC efficiency.
- Develop and maintain reusable AI frameworks, accelerators, libraries, reference implementations, and engineering playbooks.
- Drive the implementation of AI-enabled solutions in requirements analysis, test case generation, test automation, defect analysis, traceability validation, test data generation, knowledge management, documentation, and quality analytics.
- Establish and enforce standards for Responsible AI, Human-in-the-Loop controls, AI observability, model evaluation, security, and governance.
- Integrate AI solutions into DevOps and CI/CD pipelines to accelerate software delivery and maintain high quality.
- Evaluate emerging AI technologies and recommend adoption strategies for the enterprise.
- Define metrics, KPIs, ROI measures, and value realization frameworks for AI adoption.
- Provide technical leadership, mentorship, and support to engineering teams in adopting AI-first delivery practices.
- Collaborate with engineering, architecture, DevOps, security, product teams, and vendor partners to ensure seamless adoption of AI solutions.
- Ensure compliance with software engineering best practices, security guidelines, and Responsible AI principles.
- Lead the development and scaling of Agentic AI, Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), and Multi-Agent Systems within quality engineering.
- Support continuous improvement through feedback, monitoring, and operational excellence in AI-powered quality engineering initiatives.
- Report on AI adoption progress, business value, risk management, and ROI to senior leadership.

Required Skills & Experience
- 8+ years in Quality Engineering, Software Engineering, Test Automation, AI/ML, or Enterprise Technology Delivery.
- 3+ years in designing and implementing AI/ML, GenAI, or Agentic AI solutions.
- Experience with Python, FastAPI, LangChain, LangGraph, LLMs, RAG, and AI orchestration frameworks.
- Proven expertise in building reusable frameworks, accelerators, and reference implementations for AI/ML-augmented engineering.
- Strong background in SDLC, software architecture, DevOps, CI/CD, microservices, API-first design, and event-driven architecture.
- Experience establishing governance, Responsible AI, and Human-in-the-Loop controls.
- Excellent technical leadership, stakeholder management, and communication skills.

Preferred Skills & Experience
- Experience with Microsoft Azure AI, OpenAI, Azure AI Search, and cloud-native AI platforms.
- Track record in building enterprise test automation frameworks, AI observability, and monitoring solutions.
- Background in technical consulting and leading engineering transformation or AI adoption initiatives.

Education & Certifications
- Bachelor's degree or higher in Computer Science, Engineering, Data Science, AI, or related field.
- Advanced certifications in AI/ML, Cloud, or Architecture preferred.

Success Measures
- Broad adoption of AI-powered quality engineering solutions across TCoE and delivery teams.
- Measurable improvements in testing productivity, automation, software quality, and delivery speed.
- Consistent application of Responsible AI, security, governance, and observability controls.
- Clear visibility for leadership into AI adoption, business value, risk management, and ROI.

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Vacancy posted more than 2 months ago

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