## 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 - Relevant experience in software development, AI/ML engineering, or applied AI with hands-on experience building production-grade AI applications. - Strong expertise in React for developing modern, responsive, and interactive web applications. - Proficiency in Java and/or Python with hands-on experience building backend services, REST APIs, and AI-driven applications. - Experience working with LLM platforms such as OpenAI, Anthropic, Azure OpenAI, or similar foundation models. - Hands-on experience designing and implementing Retrieval-Augmented Generation (RAG) pipelines, including embeddings, vector databases, chunking strategies, retrieval optimization, and grounding techniques. - Experience with LLM orchestration frameworks such as LangChain, LlamaIndex, LangGraph, Haystack, or equivalent. - Strong knowledge of prompt engineering, structured outputs, function calling, tool integration, and agentic AI workflows. - Experience developing AI services using FastAPI, Flask, or similar backend frameworks. - Knowledge of HTML, CSS, JavaScript, asynchronous programming, testing frameworks, and API development. - Experience with LLM evaluation frameworks such as RAGAS, DeepEval, Promptfoo, LangSmith, or equivalent. - Familiarity with Git, CI/CD pipelines, Docker, Linux, and software deployment practices. - Working knowledge of at least one cloud platform such as Azure, AWS, or GCP. - Basic understanding of infrastructure security concepts, including vulnerabilities, IAM, logging, access controls, and cloud security best practices. - Understanding of responsible AI principles, including prompt injection prevention, data privacy, hallucination mitigation, output validation, and content filtering. - Familiarity with SIEM platforms, security monitoring tools, Infrastructure as Code (Terraform or Bicep), and vulnerability management concepts is an advantage. - Strong analytical, troubleshooting, communication, and problem-solving skills with the ability to work collaboratively in cross-functional teams. - Relevant cloud, AI, or security certifications are an added advantage. Responsibilities - Design, develop, and deploy AI-powered applications and intelligent assistants using Large Language Models (LLMs) to automate security and enterprise workflows. - Build scalable React-based user interfaces and dashboards for AI-driven applications and enterprise automation solutions. - Develop backend services, REST APIs, and orchestration layers using Java and/or Python to support AI capabilities. - Design and implement end-to-end Retrieval-Augmented Generation (RAG) pipelines, including document ingestion, embeddings, vector storage, retrieval optimization, and contextual response generation. - Integrate enterprise AI solutions with LLM providers such as OpenAI, Anthropic, Azure OpenAI, and other commercial or open-source models. - Develop prompt templates, system prompts, structured outputs, and agentic workflows to improve AI accuracy and user experience. - Build AI microservices and APIs that integrate with enterprise applications, security tools, monitoring platforms, ticketing systems, and operational workflows. - Implement evaluation frameworks, regression testing, and performance monitoring to continuously improve model quality, latency, reliability, and operational efficiency. - Apply responsible AI practices by implementing security controls, prompt injection protection, PII masking, access controls, audit logging, and output validation. - Automate AI operational tasks including data preparation, embedding refresh, model evaluation, health monitoring, and deployment processes. - Collaborate with engineering, DevOps, infrastructure, security, and business teams to design and deliver scalable AI-powered solutions. - Participate in code reviews, testing, debugging, documentation, and production support activities to ensure high-quality software delivery. - Continuously evaluate emerging AI technologies, frameworks, and best practices to enhance enterprise AI capabilities and accelerate innovation. - Ensure AI applications are scalable, secure, maintainable, and aligned with enterprise architecture, governance, and compliance standards. ## Qualifications Bachelor’s or master’s degree in computer science, Information Technology, or a related field.