Generative AI Engineer - LLM/NLP

Talent box labs
Multiple Locations
8 - 11 Years

Posted on: 23/05/2025

Job Description

Job Description :

We are seeking a highly skilled Generative AI Engineer with strong expertise in Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), and cloud-based machine learning applications. This role is ideal for professionals who are passionate about cutting-edge NLP solutions, building production-grade AI systems, and working in collaborative, agile environments.


Key Responsibilities :


- Design and develop AI applications using advanced LLM models (OpenAI, Anthropic, etc.)

- Build and implement Retrieval-Augmented Generation (RAG) pipelines using vector databases (Weaviate, PineCone, etc.)

- Leverage Langchain and HuggingFace to develop scalable GenAI solutions

- Work with large structured and unstructured datasets for ML training and inference

- Design and expose secure, scalable RESTful APIs (FastAPI preferred)

- Collaborate with cross-functional teams including data engineers, product managers, and domain experts

- Deploy, monitor, and optimize AI/ML models in production environments (AWS preferred)


Essential Qualifications :


- Masters degree or higher in Computer Science, Engineering, or a related field

- 3+ years of hands-on experience in Machine Learning/NLP

- Proficiency in Python, including advanced ML/NLP libraries

- Strong knowledge of prompt engineering, zero-shot and few-shot learning, and LLM fine-tuning

- 1+ years of practical experience with LLM-based application development

- Experience working with cloud platforms (AWS/Azure/GCP)

- Strong understanding of SQL and NoSQL database systems

- Exposure to DevOps, version control (Git), CI/CD pipelines


Desirable Skills :


- Experience with open-source LLMs (Llama, Mistral)

- Background working with data in regulated domains (healthcare, banking)

- Proficiency in JavaScript (front-end/back-end) is a plus

- Knowledge of Kafka, Azure Event Hub, or AWS Kinesis for real-time data processing

- Familiarity with microservices architecture


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