## 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 : 7.5+ years - Relevasnt experience in a Solutions Architect, Principal Engineer, or equivalent technical leadership role. - Proven experience designing and delivering enterprise-scale AI/ML solutions from concept through production deployment. - Strong expertise in AI application solution design, enterprise architecture, and scalable distributed systems. - Hands-on experience designing and implementing Retrieval-Augmented Generation (RAG) workflows, vector databases, prompt engineering, and LLM evaluation frameworks. - Strong understanding of Design Systems and the ability to architect scalable, reusable, and user-centric AI platforms. - Experience in data science solutioning, including model selection, classification, anomaly detection, clustering, deployment, monitoring, and optimization. - Strong knowledge of enterprise data architecture, data pipelines, data lakes, batch and real-time processing, and data quality frameworks. - Experience with Databricks, Delta Lake, or similar modern data platforms is preferred. - Proficiency in Python with working knowledge of Java, Go, or Node.js. - Experience designing REST APIs, event-driven architectures, and integrating enterprise applications such as ERP systems. - Good understanding of MLOps practices, including MLflow, model registries, model serving, inference optimization, and AI observability. - Experience with distributed tracing, logging, monitoring, and alerting for production AI systems. - Strong analytical, problem-solving, documentation, and stakeholder management skills. - Excellent communication skills with the ability to translate complex business requirements into scalable technical architectures. - Experience in fintech, compliance, tax technology, SAP data models, GSTN integrations, or explainable AI solutions is an advantage. Responsibilities - Lead discovery workshops with business, compliance, assurance, and IT stakeholders to understand business processes, data flows, and existing technology landscapes. - Analyze enterprise data ecosystems, identify integration opportunities, technical constraints, and delivery risks, and define scalable solution approaches. - Design end-to-end AI application architectures encompassing data ingestion, machine learning, LLMs, RAG workflows, APIs, and application layers. - Define architecture blueprints, component designs, API contracts, technology stack selections, and end-to-end data flow documentation. - Evaluate business use cases and determine the most appropriate implementation approach using AI, machine learning, LLMs, or rule-based solutions. - Establish architecture standards, design principles, and technical governance for enterprise AI platforms. - Provide technical leadership by conducting architecture reviews, guiding engineering teams, and ensuring adherence to architectural standards. - Resolve technical challenges, manage architectural risks, and balance technical debt with project delivery objectives. - Design scalable data ingestion and integration architectures connecting ERP systems, banking platforms, government portals, supplier systems, and enterprise applications. - Define real-time and batch data processing strategies to support operational and compliance-driven business requirements. - Architect integration frameworks that seamlessly incorporate AI outputs into existing enterprise systems while implementing fallback mechanisms for low-confidence predictions. - Collaborate closely with engineering, data science, product, and business teams to ensure successful solution delivery. - Design AI solutions with scalability, security, explainability, observability, and regulatory compliance as core architectural principles. - Drive adoption of engineering best practices, reusable design patterns, and modern AI development methodologies across the organization. - Mentor engineering teams and provide technical guidance throughout the solution lifecycle, from architecture through pilot deployment and production rollout. ## Qualifications Bachelor’s or master’s degree in computer science, Information Technology, or a related field.