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.
