Gen AI Architect

GenAI with test architect experience, test frameworks, GenAI tools/accelerators/experience in solutioning for AI use cases, RAG

Location: Bangalore, Chennai and Hyderabad

Role Overview:

As a GenAI Architect, you will lead the innovation and integration of next-generation artificial intelligence technologies within the Customer’s Quality Engineering ecosystem. Your mission is to reduce regression cycle times and drive transformative innovation by implementing AI-driven testing accelerators, intelligent agent systems, smart regression models, and predictive analytics. You will evaluate emerging AI/GenAI tools—including agents and multi-chain processing architectures—design AI-powered testing solutions and formulate a strategic AI adoption roadmap tailored for scalable, future-ready QE.

Key Responsibilities:

• Architect and implement AI accelerators to enable smart delta regression testing and predictive defect identification, significantly optimizing test cycle efficiency.
• Evaluate the feasibility and practicality of GenAI-driven test case generation using large language models and agentic systems.
• Design, develop, and integrate intelligent autonomous agenitc workflows , agents and MCP Servers into QE automation to enhance adaptability and scalability.
• Define and establish quality gates, regression SLA models, and feedback loops powered by AI-driven analytics.
• Collaborate closely with QE, data science, and software engineering teams to embed AI/ML models, agent workflows, and orchestration pipelines into testing infrastructures.
• Continuously assess and adopt advanced GenAI frameworks like LangChain, LangSmith, Flowise AI etc..
• Drive tool standardization and best practices for GenAI integration within QE processes.
• Provide technical leadership, mentorship, and evangelize AI-driven innovation across the organization.
• Ensure compliance with the Customer’s security, privacy, and governance standards.

Expertise & Qualifications:

• Deep expertise in AI-driven testing methodologies, including smart regression, predictive analytics, and AI-powered test case generation.
• Proven experience designing and deploying agentic AI systems, autonomous agents, and Multi-Chain Processing (MCP) workflows.
• Strong understanding of large language model (LLM) applications in software testing and quality assurance.
• Skilled in orchestrating AI workflows and pipelines using frameworks like LangChain, LangSmith, or custom agent architectures.
• Solid background in software testing principles, automation frameworks, and Quality Engineering processes.
• Familiarity with advanced GenAI tools such as AutoGPT, BabyAGI, AgentGPT, and orchestration frameworks.
• Excellent problem-solving, communication, and collaboration skills across cross-functional teams.

Tools & Technical Skills:

• AI/ML Frameworks: TensorFlow, PyTorch, scikit-learn.
• Generative AI Platforms & LLMs: OpenAI GPT models (e.g., GPT-4, ChatGPT, Codex), Hugging Face Transformers, Google Gemini, Anthropic Claude.
• Agent Frameworks & Autonomous Systems: AutoGPT, BabyAGI, LangChain Agents, LangSmith, AgentGPT, Flowise AI, and similar.
• MCP Servers: Design and develop MCP servers
• Workflow Automation & Orchestration Tools: n8n, Zapier, Apache Airflow, Prefect — used to build, automate, and orchestrate AI agents and GenAI workflows within QE pipelines.
• AI-Enhanced Test Automation: Integration of AI/ML into testing frameworks and CI/CD pipelines (e.g., Jenkins, GitLab CI).
• Data Analytics & Visualization: Python (Pandas, NumPy), Jupyter Notebooks, Power BI, Tableau.
• Programming Languages: Python, R, Java, Scala.
• Cloud AI Services: AWS SageMaker, Azure Machine Learning, Google AI Platform.

Preferred Qualifications:

• Prior experience integrating AI agents and multi-chain AI workflows in enterprise QE or software development environments.
• Demonstrated success in deploying GenAI solutions to accelerate regression testing and defect prediction.
• Certifications in AI/ML, software architecture, or quality engineering.
• Experience with security and compliance in AI-enabled testing solutions.