Job Description
Job Description
The AI Solution Engineer will design, build, and scale Agentic AIdriven Quality Engineering solutions that automate and optimize testing, quality insights, and decision-making. This role focuses on advanced Retrieval-Augmented Generation (RAG), multi-model LLM orchestration, and autonomous AI agents, enabling next-generation Quality Intelligence platforms integrated with enterprise QE ecosystems.
Responsibilities
Design and develop Agentic AI systems for Quality Engineering use cases such as test generation, test optimization, defect prediction, root-cause analysis, and quality insights.
Build and optimize advanced RAG pipelines using structured, semi-structured, and unstructured test and quality data.
Implement multi-LLM architectures, including model selection, routing, chaining, and fallback strategies across proprietary and open-source LLMs.
Develop autonomous and semi-autonomous AI agents with tool usage, memory, planning, and reasoning capabilities.
Integrate AI solutions with test automation frameworks, CI/CD pipelines, and QE platforms.
Ensure scalability, observability, security, and governance of AI systems in production environments.
Collaborate with QE, DevOps, and Platform teams to embed AI into enterprise quality workflows.
Contribute to AI solution architecture, best practices, and technical roadmaps.
Qualifications
Strong hands-on experience in Python and building AI-driven backend services.
Proven experience with LLM frameworks such as LangChain, LlamaIndex, Semantic Kernel, or similar.
Deep understanding of RAG architectures, embeddings, chunking strategies, and vector databases
Experience working with multiple LLMs (OpenAI, Azure OpenAI, open-source models) and implementing model orchestration and evaluation.
Solid knowledge of Agentic AI concepts: planning, tool invocation, memory, reasoning loops, and feedback mechanisms.
Experience integrating AI solutions with Quality Engineering, test automation, or SDLC tools.
Familiarity with cloud-native architectures, APIs, containerization, and MLOps/LLMOps practices.
Nice-to-Have
Experience building Quality Intelligence platforms or AI-driven QE CoE solutions.
Knowledge of test frameworks, CI/CD pipelines, and observability tools.
Exposure to AI governance, security, and responsible AI practices.
About Us
At Zensar, we're experience-led everything. We are committed to conceptualizing, designing, engineering, marketing, and managing digital solutions and experiences for over 130 leading enterprises. We are a company driven by a bold purpose: Together, we shape experiences for better futures. Whether for our clients, our people, or the world around us, this belief powers everything we do. At the heart of our culture is ONE with Client - a set of four core values that reflect who we are and how we work: One Zensar, Nurturing, Empowering, and Client Focus.
Part of the $4.8 billion RPG Group, we're a community of 10,000+ innovators across 30+ global locations, including Milpitas, Seattle, Princeton, Cape Town, London, Zurich, Singapore, and Mexico City. Explore Life at Zensar and join us to Grow. Own. Achieve. Learn. to be the best version of yourself.
We believe the best work happens when individuality is celebrated, growth is encouraged, and well-being is prioritized. We are an equal employment opportunity (EEO) and affirmative action employer, committed to creating an inclusive workplace. All qualified applicants will be considered without regard to race, creed, color, ancestry, religion, sex, national origin, citizenship, age, sexual orientation, gender identity, disability, marital status, family medical leave status, or protected veteran status.
