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Senior Staff Engineer (Generative AI, Machine Learning)

Nagarro

Bengaluru

7-12 years

Today

$55.4K–89.2K/yr

Full-time

Onsite

Skills Required LLM

RAG

Gen AI

Agentic AI

LangChain

Prompt Engineering

LLM Evaluation Frameworks

Vector Database

Embeddings

LangGraph

Machine Learning

AI

LangSmith

FAISS

Azure AI Search

Description Nagarro is hiring a Senior Staff Engineer focused on Generative AI, LangChain, LangGraph, and Machine Learning. The role is based in Bengaluru, India, in a full-time setup within the South Asia service region.

Company: Nagarro

Role: Senior Staff Engineer (Generative AI, Langchain + Langraph, Machine Learning)

Location: Bengaluru, India | Full-time | Service Region: South Asia

Experience

- 7.5 to 12 years of overall experience in Data Science, Machine Learning, and Artificial Intelligence
- Hands-on experience with Generative AI fundamentals, Large Language Models, and Agentic AI systems
- Proven expertise in developing GenAI applications using LangChain, LangGraph, and associated ecosystem tools
- Experience designing and implementing Retrieval Augmented Generation solutions
- 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
- 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
- Valuable 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

Qualification

- Bachelor’s degree in computer science, Information Technology, or a related field
- Master’s degree in computer science, Information Technology, or a related field

Responsibilities

- Design, develop, and deploy enterprise-grade Generative AI and Agentic AI solutions
- 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 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

More Skills instruction tuning, ReAct frameworks, reasoning strategies, planning loops, self-reflection, model assessment, GenAI quality measurement, tracing, monitoring, debugging, regression testing, performance optimization, OpenSearch, PGVector, intelligent agents, tool-calling agents, planner-executor frameworks, multi-agent systems, hierarchical agent architectures, memory architectures, episodic memory, semantic memory, long-term vector-based memory, APIs, enterprise applications, knowledge repositories, external tools, feature engineering, model development, hyperparameter tuning, model evaluation, structured data, unstructured data, predictive analytics, MLOps, model monitoring, data drift detection, concept drift analysis, model quality management, AWS, Azure, Databricks, Git, GitHub, Model Context Protocol, MCP, code execution environments, AI Engineering

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