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.
