👋🏼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!

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.

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