## Company Description 👋🏼We're Nagarro. We are a Digital Product Engineering company that is scaling in a big way! We build products, services, and experiences that inspire, excite, and delight. We work at a scale — across all devices and digital mediums, and our people exist everywhere in the world (18500+ experts across 40 countries, to be exact). Our work culture is dynamic and non-hierarchical. We are looking for great new colleagues. That is where you come in! ## Job Description Requirements - Experience : 13+ years - Strong experience in AI/ML, Data Science, Intelligent Automation, or Generative AI, including enterprise-scale solution architecture. - Strong expertise in designing and implementing Agentic AI solutions and AI application architectures. - Hands-on experience with multi-agent systems, autonomous workflows, and AI orchestration frameworks. - Deep understanding of reasoning frameworks such as ReAct, Plan-and-Execute, Reflection, and Tree-of-Thoughts. - Strong experience designing enterprise AI applications leveraging RAG, GraphRAG, Knowledge Graphs, Semantic Search, and Enterprise Search. - Expertise in LLM-powered applications, prompt engineering, embeddings, semantic retrieval, model evaluation, and fine-tuning. - Hands-on experience with AI application frameworks such as LangGraph, LangChain, CrewAI, AutoGen, Semantic Kernel, LlamaIndex, OpenAI Agent SDK, Google ADK, and MCP (Model Context Protocol). - Strong programming skills in Python, along with proficiency in Java, JavaScript/TypeScript, C#, Go, or similar programming languages. - Experience building production-grade AI applications using APIs, microservices, distributed systems, and cloud-native architectures. - Strong knowledge of vector databases, graph databases, enterprise data platforms, and AI engineering best practices. - Experience implementing AI-assisted software development workflows, developer productivity tools, and AI-enabled application development. - Expertise in cloud platforms such as AWS, Azure, or GCP, along with Kubernetes, containerization, CI/CD, DevSecOps, MLOps, LLMOps, and AgentOps. - Strong understanding of AI governance, observability, monitoring, evaluation frameworks, and responsible AI practices. - Excellent consulting, stakeholder management, communication, and presentation skills. - Proven ability to lead cross-functional teams, mentor technical professionals, and drive enterprise AI transformation initiatives. - Experience across industries such as Financial Services, Retail, Telecom, Manufacturing, Healthcare, or CPG is preferred. - Experience with Knowledge Graphs, ontology design, semantic data models, AI evaluation frameworks, or open-source AI contributions is an added advantage. - Cloud Architect and AI certifications are preferred. Responsibilities - Design and architect enterprise-scale Agentic AI and Generative AI solutions aligned with business objectives. - Define scalable architectures for multi-agent collaboration, autonomous workflows, human-in-the-loop systems, and intelligent orchestration. - Lead the design and implementation of advanced reasoning frameworks, agent communication protocols, memory management, and tool integration strategies. - Establish AI governance frameworks, guardrails, evaluation methodologies, observability standards, and production best practices. - Architect enterprise knowledge systems leveraging RAG, GraphRAG, Knowledge Graphs, Semantic Search, and Enterprise Search technologies. - Design retrieval architectures that integrate structured and unstructured enterprise data sources. - Define memory architectures, context management strategies, and enterprise knowledge frameworks for AI applications. - Evaluate and optimize LLM selection, orchestration, inference strategies, and model performance across commercial and open-source platforms. - Architect scalable AI platforms supporting enterprise-wide AI workloads, reusable accelerators, and reference architectures. - Establish engineering standards for AI Engineering, LLMOps, AgentOps, MLOps, deployment, monitoring, and lifecycle management. - Define integration patterns using APIs, microservices, event-driven architectures, and workflow orchestration frameworks. - Drive adoption of AI-enabled software development practices across the SDLC, including coding, testing, documentation, deployment, and maintenance. - Act as a trusted advisor to business and technology stakeholders on AI strategy, architecture, and enterprise transformation initiatives. - Conduct architecture assessments, discovery workshops, AI strategy engagements, and solution design sessions. - Lead Proof of Concepts (PoCs), MVPs, and enterprise AI implementations from concept through production deployment. - Mentor architects, engineers, and data scientists while promoting engineering excellence and architectural best practices. - Support solution development activities including proposals, RFP responses, effort estimation, and executive presentations. - Collaborate with cross-functional teams to deliver scalable, secure, and high-performance AI solutions that meet business and technology goals. - Continuously evaluate emerging AI technologies, frameworks, and industry trends to drive innovation and enhance enterprise AI capabilities. ## Qualifications Bachelor’s or master’s degree in computer science, Information Technology, or a related field.