Generative AI Engineer

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 Proficiency in Python and experience with AI/ML frameworks like PyTorch, TensorFlow, or JAX . Strong understanding of transformer-based architectures and deep learning fundamentals . Hands-on experience with LLMs (GPT, LLaMA, Falcon, Claude, Mistral, etc.) and API integration . Familiarity with LangChain , Hugging Face Transformers , and vector search systems (FAISS, Pinecone, Chroma, Milvus). Experience in building and optimizing prompt-based systems and custom model fine-tuning . Working knowledge of cloud infrastructure and DevOps tools (AWS S3, EC2, Lambda, Docker, Kubernetes). Proficiency with MLOps tools such as MLflow, DVC, or Weights & Biases. Understanding of data security, scalability , and API versioning principles . Bonus: Experience in text-to-image (Stable Diffusion, DALL·E) or text-to-speech/speech-to-text systems (Whisper, ElevenLabs). Preferred Qualifications Bachelor’s or Master’s Degree in Computer Science, Artificial Intelligence, Data Science, or a related discipline . Minimum of 3 years of experience in machine learning or AI , with at least 1 year in Generative AI or NLP . Proven track record of building and deploying AI models into production. Strong problem-solving and analytical skills, with the ability to apply research into practical product implementations. Portfolio, GitHub repositories, or research publications demonstrating prior AI/ML projects will be an added advantage.