Generative AI Engineer

We are seeking an experienced Generative AI Engineer to design, develop, and optimize AI models for text, image, video, and audio generation. The ideal candidate should have expertise in deep learning, natural language processing (NLP), transformer models (GPT, BERT, LLaMA, etc.), and multimodal AI. This role involves working with large-scale datasets, fine-tuning AI models, and deploying scalable AI solutions.

Key Responsibilities:
Design, develop, and fine-tune Generative AI models for text, image, video, and audio synthesis.
Work with transformer architectures such as GPT, BERT, T5, Stable Diffusion, and CLIP.
Implement and optimize LLMs (Large Language Models) using Hugging Face, OpenAI, or custom architectures.
Develop AI-powered chatbots, virtual assistants, and content generation tools.
Work with diffusion models, GANs (Generative Adversarial Networks), and VAEs (Variational Autoencoders) for creative AI applications.
Optimize AI models for performance, inference speed, and cost efficiency in cloud or edge environments.
Deploy AI models using TensorFlow, PyTorch, ONNX, and MLflow on AWS, Azure, or GCP.
Work with vector databases (FAISS, Pinecone, Weaviate) and embedding-based search techniques.
Fine-tune models using RLHF (Reinforcement Learning with Human Feedback) for better alignment.
Collaborate with data scientists, ML engineers, and product teams to integrate AI capabilities into applications.
Ensure AI model security, bias mitigation, and ethical AI practices.
Stay updated with the latest advancements in Generative AI, foundation models, and prompt engineering.
Required Skills & Qualifications:
6+ years of experience in AI, machine learning, and deep learning.
Strong expertise in Generative AI models and transformer architectures.
Proficiency in Python, TensorFlow, PyTorch, and Hugging Face libraries.
Experience with NLP, text embeddings, and retrieval-augmented generation (RAG).
Knowledge of vector databases, embeddings, and scalable model serving (FastAPI, Triton, Ray Serve).
Experience with GPU acceleration (CUDA, TensorRT, ONNX optimization) for AI workloads.
Familiarity with cloud-based AI services like AWS Bedrock, Azure OpenAI, or Google Vertex AI.
Strong understanding of data preprocessing, annotation, and model evaluation metrics.
Experience working with large-scale datasets and distributed training techniques.
Strong problem-solving skills and ability to work in Agile/DevOps environments.
Preferred Qualifications:
Experience with multimodal AI (text-to-image, text-to-video, speech synthesis).
Knowledge of RLHF, prompt engineering, and AI-assisted code generation.
Certifications in Machine Learning, AI, or Cloud AI services.