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 39 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:
- 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.
Additional Information
Bachelor’s or master’s degree in computer science, Information Technology, or a related field.
