AI ML TESTING

Requirements

• Total 8+ years of IT experience across SDLC, STLC, Quality Engineering, Platform Engineering, or related areas
• Minimum 3+ years of hands-on AI/ML and Generative AI implementation experience
• Strong domain expertise in Quality Engineering to identify problem statements and design relevant AI solutions across FT, PT, automation, and QA operations, with mandatory deep understanding of QA/NFT domains
• Experience in designing and implementing enterprise-scale AI solutions.
• Build and implement Generative AI / LLM-based solutions including copilots, assistants, autonomous agents, and QA accelerators with focus on agentic workflows
• Design end-to-end AI/ML solutions aligned with enterprise standards
• Design and implement Retrieval-Augmented Generation (RAG) solutions
• Build and manage knowledge bases using test artifacts, logs, and reports for context-aware reasoning
• Work with vector databases, embeddings, and retrieval strategies
• Build agent-based systems capable of autonomous interpretation, multi-step reasoning, and decision-making with minimal prompting
• Lead the design and implementation ofcomplex AI/ML models, including NLP, computer vision, and predictive analytics.
• Develop AI strategy and roadmap based on business priorities and emerging technologies.
• Experience with agent orchestration frameworks such as LangChain, LangGraph, Semantic Kernel, and LlamaIndex
• Ensure solutions are production-grade with high performance, scalability, and fault tolerance
• Integrate AI systems via APIs, microservices, and enterprise platforms
• Enable multi-model orchestration (GPT, Claude, Gemini, etc.) and model-agnostic architecture design
• Establish MLOps / LLMOps practices including CI/CD pipelines and model lifecycle management
• Implement monitoring, drift detection, feedback loops, and continuous improvement mechanisms
• Ensure AI system performance optimization including latency, token efficiency, and cost optimization
• Define governance for data security, compliance, Responsible AI, and explainability
• Collaborate with QA, development, and business stakeholders to translate requirements into AI solutions
• Mentor team members and drive AI adoption across teams
• Conduct architecture reviews and technical design sessions