Job Description

We are seeking a skilled and innovative AI Engineer / Senior AI Engineer to design, develop, evaluate, and deploy enterprise AI solutions focused on Quality Engineering, Performance Testing, Test Automation, and Operational Efficiency. The ideal candidate must have strong domain expertise in Functional Testing (FT), Performance Testing (PT), Automation, and QA operations, along with hands-on experience in building Generative AI / LLM-based and agentic solutions. This is a hands-on engineering and solutioning role requiring the ability to translate QA/NFT challenges into scalable AI-driven solutions

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 of complex 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
Experience in enterprise AI transformation programs
Experience in vector databases and RAG pipelines
Knowledge of Responsible AI and Explainable AI frameworks
Experience with multi-modal AI (text, image, logs, telemetry)
Experience in performance engineering for AI systems
Hands-on experience with LangChain, LangGraph, LlamaIndex, Semantic Kernel, and agent frameworks
Strong experience with QA tools such as Selenium, Playwright, JMeter, NeoLoad, LoadRunner, and observability tools like Splunk or Dynatrace
Strong understanding of testing lifecycles, bottlenecks, and tool ecosystems
Identify, design, develop, and deploy AI use cases across FT, PT, automation, and operations
Build end-to-end agentic systems (not just prompt-based solutions)
Drive enterprise-grade, production-ready AI architecture

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