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Nagarro
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Staff Engineer, LLM,AI

Staff Engineer, LLM,AI

Nagarro

6 - 11 years

25 - 30 Lacs

gurugram

Posted:3 hours ago| Platform:

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Skills Required

cortex automation version control compliance analytical machine learning research open source aws python

Work Mode

Work from Office

Job Type

Full Time

Job Description

Total Experience: 6+ years

in Data Science / Applied Machine Learning, including

1-2+ years in Generative AI / LLM-based solutions

. Strong proficiency in

Python

, with hands-on experience using

LangChain, Pandas, NumPy, scikit-learn, Hugging Face Transformers, Pydantic

, etc. Experience deploying

GenAI workloads on AWS

, including

Amazon Bedrock, SageMaker, Lambda, ECS, API Gateway, Step Functions, CloudWatch

. Deep understanding of

LLM architectures

, tokenization,

RAG

, embeddings, and vector databases such as

FAISS, Qdrant, Pinecone, OpenSearch

. Hands-on experience implementing

agent-based architectures

and multi-step reasoning pipelines. Strong expertise in

data wrangling, feature engineering

, and building ML models for

classification, regression, clustering, and NLP

. Experience with

CI/CD

, API development, and integrating GenAI models into production-grade applications. Familiarity with

LangGraph, AutoGPT, CrewAI

, or other custom agent frameworks. Knowledge of vector databases such as

Qdrant, Snowflake Cortex

, embedding models, and custom

RAG pipelines

. Prior contributions to

open-source GenAI projects

or research work in LLM/NLP domains. Hands-on experience with

MLOps

, including model versioning and monitoring tools (

MLflow, Weights & Biases, SageMaker Model Monitor

). Exposure to

fine-tuning and parameter-efficient tuning

(LoRA, QLoRA) of LLMs. Understanding of

data privacy, PII redaction, security, and compliance

considerations in GenAI applications. Strong analytical thinking, problem-solving, and communication skills.

RESPONSIBILITIES:

Build, deploy, and optimize

Generative AI and LLM-based applications

for scalable enterprise use cases. Design and implement

RAG systems

, vector search pipelines, and agent-based reasoning workflows. Architect and develop

end-to-end ML/GenAI solutions

including data ingestion, preprocessing, experimentation, evaluation, and deployment. Work with cross-functional teams to define problem statements and translate business requirements into technical solutions. Develop and maintain

GenAI-powered APIs, microservices

, and automation workflows using AWS services. Optimize model performance, cost efficiency, token usage, prompt structures, and overall system reliability. Build reusable

GenAI components

including prompt templates, toolchains, memory modules, evaluators, and monitoring dashboards. Implement robust

MLOps practices

for version control, experiment tracking, CI/CD pipelines, and automated deployment. Ensure high-quality production readiness through

testing, model validation, drift detection, and continuous monitoring

. Conduct research, POCs, and benchmarking of emerging

LLMs, embeddings, vector databases

, and agent frameworks. Prepare documentation, architecture diagrams, and best practices for internal and client-facing teams. Collaborate with engineering, product, and cloud teams to ensure seamless integration of GenAI features into applications. Mentor junior engineers and contribute to knowledge sharing, code reviews, and innovation initiatives. Ensure compliance with

security, privacy, and responsible AI

guidelines across all GenAI implementations.

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