👋🏼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!

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.

Bachelor’s or master’s degree in computer science, Information Technology, or a related field.