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 39 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 : 5.5+years • Experience in software development, AI/ML engineering, or applied AI, including at least 1–2 years of hands-on experience building production-grade LLM applications. • Expert-level proficiency in React.js for developing scalable, responsive, and reusable frontend applications. • Strong hands-on experience with Python for AI development, automation, backend services, and API integration. • Working knowledge of Java for backend services, REST API development, and enterprise application integration. • Practical experience with LLM providers and APIs, including OpenAI, Anthropic, Azure OpenAI, Vertex AI, or equivalent platforms. • Hands-on experience with prompt engineering, system prompts, structured outputs, JSON schemas, function calling, and tool integration. • Strong understanding of LLM application development, AI assistants, agentic workflows, and enterprise AI integration. • Experience with at least one LLM orchestration framework such as LangChain, LlamaIndex, LangGraph, Haystack, or equivalent. • Hands-on experience implementing RAG pipelines, including embeddings, document chunking, retrieval tuning, grounding, and vector database integration. • Experience with at least one vector database or search platform, such as pgvector, Pinecone, Weaviate, Chroma, FAISS, Azure AI Search, or Vertex AI Vector Search. • Working knowledge of LLM evaluation methodologies, including evaluation datasets, regression testing, and frameworks such as RAGAS, DeepEval, Promptfoo, or LangSmith. • Strong understanding of AI application testing, debugging, observability, performance optimization, and cost management. • Experience with FastAPI, Flask, asynchronous programming, Python packaging, and testing frameworks. • Basic frontend development knowledge of HTML, CSS, and JavaScript, along with backend API development experience. • Knowledge of Shell scripting, Linux fundamentals, Git, CI/CD pipelines, and Docker. • Exposure to at least one cloud platform such as Microsoft Azure, AWS, or Google Cloud Platform. • Basic understanding of Infrastructure Security concepts, including vulnerabilities, patch management, security logs, IAM, and access controls. • Awareness of responsible AI and security practices, including prompt injection, data leakage, hallucinations, bias, and content filtering. • Familiarity with SIEM platforms such as Microsoft Sentinel or Splunk, including the ability to write KQL or SPL queries for AI-assisted security detection and investigation. • Basic understanding of Cloud Security Posture Management (CSPM) and cloud security controls such as Network Security Groups (NSGs), Web Application Firewalls (WAF), IAM policies, and conditional access. • Understanding of Infrastructure as Code (IaC) tools such as Terraform or Bicep for cloud-native AI service deployment and configuration. • Exposure to data privacy and regulatory requirements applicable in India, including the DPDP Act and RBI IT Risk Framework, particularly in handling personal and sensitive data within AI systems. • Familiarity with vulnerability management concepts, including CVE, CVSS, EPSS scoring, CISA KEV catalogue, and patch prioritization workflows. • Strong analytical and problem-solving skills, with the ability to debug LLM behavior across prompts, retrieval pipelines, and tool integrations. • Ability to interpret evaluation metrics, application logs, and security telemetry to improve AI quality, reliability, and operational efficiency. • Demonstrated ownership of AI solutions from prototype to production, including evaluation, observability, testing, and documentation. • Strong learning agility and adaptability to evolving AI models, frameworks, tools, and development practices. • Excellent written and verbal communication skills, with the ability to collaborate effectively across technical and business teams. • Certifications such as CISSP, Associate of ISC², CEH, Google Professional Machine Learning Engineer, AWS Certified Machine Learning – Specialty, Microsoft Azure Administrator Associate (AZ-104), CCSP, or Google Associate Cloud Engineer are preferred. Responsibilities • Design, develop, and deploy LLM-powered features, AI assistants, and intelligent automation solutions to support Infrastructure Security (InfraSec) workflows. • Build AI-driven capabilities for vulnerability summarization, log triage, remediation recommendations, security policy reviews, configuration assessments, and natural-language querying of security data. • Develop scalable, responsive, and reusable frontend applications using React.js for AI-powered internal tools and dashboards. • Build and maintain backend AI services and APIs using Python, with Java-based backend development wherever required. • Integrate LLM providers such as OpenAI, Anthropic, Azure OpenAI, Vertex AI, and open-source models into enterprise applications. • Design and optimize prompt templates, system prompts, structured-output schemas, and function-calling workflows. • Implement end-to-end Retrieval-Augmented Generation (RAG) pipelines, including document chunking, embeddings, vector store integration, retrieval optimization, and response grounding. • Develop AI agents and tool-calling workflows using frameworks such as LangChain, LlamaIndex, LangGraph, Haystack, or equivalent technologies. • Build evaluation frameworks, golden datasets, and regression test suites to measure LLM accuracy, hallucination rates, response quality, latency, and cost. • Continuously improve prompt engineering and AI outputs through offline and online evaluations. • Implement responsible AI and security controls, including prompt-injection protection, PII redaction, output filtering, rate limiting, audit logging, and access management. • Integrate AI services with vulnerability scanners, ticketing platforms, SIEM solutions, monitoring tools, and other security infrastructure to enable automated security workflows. • Automate AI/ML operations activities such as data preparation, embedding refresh, evaluation runs, health checks, and monitoring using Python and Shell scripting. • Test, debug, and optimize AI applications to ensure reliability, scalability, performance, and cost efficiency. • Maintain technical documentation, source code repositories, deployment artifacts, and operational procedures. • Collaborate with security, infrastructure, engineering, DevOps, and data teams to translate business requirements into practical AI-driven solutions. • Apply infrastructure security principles and cloud security controls while designing and deploying AI services. • Evaluate emerging LLM models, frameworks, prompting techniques, and evaluation methodologies to continuously improve solution quality and security posture. Qualifications Bachelor’s or master’s degree in computer science, Information Technology, or a related field.