Staff Engineer, Machine Learning, Gen AI

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 scale across all devices and digital mediums, and our people exist everywhere in the world (17500+ 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:

• Strong expertise in

  backend engineering

  using Python and modern backend frameworks.
• Solid understanding of

  ML-adjacent systems

  , including embeddings, retrieval, ranking, and evaluation workflows.
• Hands-on experience with

  RAG architectures

  , including document ingestion, chunking strategies, embeddings generation, and retrieval pipelines.
• Experience with

  vector similarity search

  and retrieval quality metrics (precision, recall, nDCG, MRR).
• Familiarity with AI-backed use cases such as

  search, recommendations, assistants, and personalization systems

  .
• Hands-on experience with

  ML/AI infrastructure and platforms

  , including model integration, feature/metadata services, and offline/online parity.
• Knowledge of

  ML system observability

  , evaluation hooks, data drift monitoring, and reliability metrics.
• Strong understanding of

  distributed systems fundamentals

  : API design, idempotency, concurrency, retries, rate limiting, and circuit breakers.
• Experience with

  vector databases

  such as FAISS, Pinecone, Milvus, and hybrid retrieval systems.
• Familiarity with orchestration and AI tooling frameworks such as

  LangChain, LlamaIndex

  , or similar.
• Strong backend integration capabilities using

  REST APIs, Docker, Kubernetes, and microservices architectures

  .
• Preferred exposure to

  cloud-native environments

  and production-grade observability stacks.

RESPONSIBILITIES:

• Understanding the clients business use cases and technical requirements and converting them into

  scalable backend and AI system designs

  .
• Mapping architectural and technical decisions to requirements and translating them clearly to engineering teams.
• Identifying multiple backend and AI system design options and narrowing down the most effective solution based on quality, latency, and cost trade-offs.
• Defining guidelines and benchmarks for

  non-functional requirements

  such as performance, reliability, scalability, and observability.
• Writing and reviewing design documents covering

  overall architecture, retrieval pipelines, backend services, and system integrations

  .
• Reviewing architecture and design across extensibility, scalability, security, design patterns, reliability, and NFRs, ensuring best practices are followed.
• Designing and developing end-to-end solutions for defined functional and non-functional requirements using appropriate

  backend and AI technologies

  .
• Understanding and applying

  technology integration scenarios

  , including ML model interfaces, feature services, and evaluation pipelines.
• Resolving issues raised during code and design reviews through systematic root-cause analysis and well-justified technical decisions.
• Carrying out

  POCs

  to validate proposed backend architectures, retrieval strategies, and AI system designs.