Senior Staff Engineer (GenAI, Langchain + Langraph, Machine Learning)

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 (18000+ experts across 36 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

• 7.5 to 12 years of overall experience in Data Science, Machine Learning, and Artificial Intelligence.
• Strong hands-on experience with Generative AI fundamentals, Large Language Models (LLMs), and Agentic AI systems.
• Proven expertise in developing GenAI applications using LangChain, LangGraph, and associated ecosystem tools.
• Experience designing and implementing Retrieval Augmented Generation (RAG) solutions, including retrieval, reranking, chunking, memory management, and context optimization.
• Strong understanding of prompt engineering techniques, including instruction tuning, ReAct frameworks, reasoning strategies, planning loops, and self-reflection mechanisms.
• Hands-on experience with LLM evaluation frameworks, model assessment, and GenAI quality measurement methodologies.
• Experience using LangSmith for tracing, monitoring, debugging, evaluation, regression testing, and performance optimization of GenAI applications.
• Strong knowledge of Vector Databases and Embeddings, including FAISS, Azure AI Search, OpenSearch, PGVector, or similar technologies.
• Experience building intelligent agents, tool-calling agents, planner-executor frameworks, multi-agent systems, and hierarchical agent architectures.
• Good understanding of memory architectures, including episodic memory, semantic memory, and long-term vector-based memory systems.
• Experience integrating AI agents with APIs, enterprise applications, knowledge repositories, and external tools.
• Strong foundation in classical Machine Learning concepts, including feature engineering, model development, hyperparameter tuning, and model evaluation.
• Experience working with structured and unstructured datasets for predictive and analytical use cases.
• Understanding of MLOps concepts, including model monitoring, data drift detection, concept drift analysis, and model quality management.
• Hands-on experience with cloud platforms such as AWS, Azure, or Databricks.
• Proficiency with version control systems and collaborative development tools such as Git and GitHub.
• Strong problem-solving, analytical, communication, and stakeholder management skills.
• Candidate should have an official notice period of 30 days or less and must be able to join within one month.

Responsibilities

• Design, develop, and deploy enterprise-grade Generative AI and Agentic AI solutions using modern LLM frameworks and tools.
• Build scalable GenAI applications leveraging LangChain, LangGraph, and related ecosystem technologies.
• Design and implement advanced RAG architectures to improve response quality, grounding, and knowledge retrieval accuracy.
• Develop and optimize prompt engineering strategies to enhance reasoning, planning, tool usage, and response generation capabilities.
• Build intelligent agents capable of tool calling, workflow orchestration, task planning, and autonomous decision-making.
• Develop multi-agent systems and agent collaboration frameworks for complex business workflows.
• Implement memory-driven agent architectures supporting contextual awareness and long-term knowledge retention.
• Create evaluation frameworks to measure performance, reliability, robustness, and business effectiveness of AI solutions.
• Establish monitoring, tracing, testing, and observability frameworks using LangSmith and related tools.
• Build and integrate Model Context Protocol (MCP) based services and external tool integrations.
• Enable AI systems to interact with APIs, applications, code execution environments, and enterprise knowledge sources.
• Apply Machine Learning techniques to solve business problems involving structured and unstructured data.
• Perform model development, feature engineering, model optimization, validation, and performance analysis.
• Collaborate closely with engineering, architecture, and cross-functional teams to productionize AI and ML solutions.
• Ensure scalability, security, maintainability, and reliability of AI-powered applications.
• Support MLOps initiatives, including model monitoring, drift detection, performance tracking, and continuous improvement.
• Maintain comprehensive technical documentation, coding standards, and quality assurance practices throughout the development lifecycle.
• Stay current with emerging trends, frameworks, tools, and best practices in Generative AI, Agentic AI, Machine Learning, and AI Engineering.