Senior Staff Engineer - Generative AI

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 - 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.