Generative AI Engineer (3+ Years Experience)

Company : Nexoria Techworks

Location : Bangalore, Hyderabad, Pune, Noida

Experience : 3+ Years

Employment Type : Full-Time, Onsite / Hybrid

About Nexoria Techworks

Nexoria Techworks is a rapidly growing technology company focused on building intelligent, scalable, and high-impact digital solutions powered by Artificial Intelligence. We work on cutting-edge innovations across AI, automation, and data-driven systems that help organizations accelerate digital transformation. Our mission is to create next-generation AI-driven platforms that deliver measurable business outcomes.

We are now expanding our AI team and looking for an experienced Generative AI Engineer with strong hands-on experience in building and deploying advanced generative models and LLM-based applications.

Role Overview

The Generative AI Engineer will be responsible for designing, training, and optimizing AI models that generate text, images, audio, or other content. This role requires a strong understanding of deep learning architectures, NLP systems, transformer-based models, and their application to real-world problems. The ideal candidate should have prior experience implementing and scaling generative models in production environments.

Key Responsibilities

• Design, develop, and fine-tune Generative AI models such as Large Language Models (LLMs), diffusion models, or transformers for text, image, or multimodal outputs.
• Build Retrieval-Augmented Generation (RAG) pipelines using vector databases like FAISS, Pinecone, or Chroma.
• Develop and implement prompt engineering strategies and optimization frameworks for task-specific outputs.
• Collaborate with the product and engineering teams to integrate AI models into scalable systems and APIs .
• Conduct research and experimentation on state-of-the-art generative architectures including GPT, LLaMA, Mistral, and Stable Diffusion.
• Develop automation pipelines using LangChain, Hugging Face Transformers , or equivalent frameworks.
• Deploy and monitor models using cloud platforms such as AWS, GCP, or Azure, ensuring performance, reliability, and cost-efficiency.
• Implement MLOps practices for model training, evaluation, deployment, and lifecycle management.
• Collaborate closely with data engineers and scientists for data collection, preprocessing, and feature engineering.
• Document model architectures, training methodologies, and deployment strategies for internal knowledge sharing.

Required Technical Skills

Preferred Qualifications