Thane, Maharashtra, India
Information Technology
Full-Time
TrueFan AI
Overview
As a Senior Machine Learning Engineer, you will be responsible for designing, developing, and deploying cutting-edge models for end-to-end content generation, including AI-driven image/video generation, lipsyncing, and multimodal AI systems. You will work on the latest advancements in deep generative modeling to create highly realistic and controllable AI-generated media.
Responsibilities
Responsibilities
- Research & Develop : Design and implement state-of-the-art generative models, including Diffusion Models, 3D VAEs and GANs for AI-powered media synthesis.
- End-to-End Content Generation : Build and optimize AI pipelines for high-fidelity image/video generation and lipsyncing using diffusion and autoencoder models.
- Speech & Video Synchronization : Develop advanced lipsyncing and multimodal generation models that integrate speech, video, and facial animation for hyper-realistic AI-driven content.
- Real-Time AI Systems : Implement and optimize models for real-time content generation and interactive AI applications using efficient model architectures and acceleration techniques.
- Scaling & Production Deployment : Work closely with software engineers to deploy models efficiently on cloud-based architectures (AWS, GCP, or Azure).
- Collaboration & Research : Stay ahead of the latest trends in deep generative models, diffusion models, and transformer-based vision systems to enhance AI-generated content quality.
- Experimentation & Validation : Design and conduct experiments to evaluate model performance, improve fidelity, realism, and computational efficiency, and refine model architectures.
- Code Quality & Best Practices : Participate in code reviews, improve model efficiency, and document research findings to enhance team knowledge-sharing and product development.
- Bachelor's or Masters degree in Computer Science, Machine Learning, or a related field.
- 3+ years of experience working with deep generative models, including Diffusion Models, 3D VAEs, GANs and autoregressive models.
- Strong proficiency in Python and deep learning frameworks such as PyTorch.
- Expertise in multi-modal AI, text-to-image, and image-to-video generation, audio to lipsync
- Strong understanding of machine learning principles and statistical methods.
- Good to have experience in real-time inference optimization, cloud deployment, and distributed training.
- Strong problem-solving abilities and a research-oriented mindset to stay updated with the latest AI advancements.
- Familiarity with generative adversarial techniques, reinforcement learning for generative models, and large-scale AI model training.
- Experience with transformers and vision-language models (e.g., CLIP, BLIP, GPT-4V).
- Background in text-to-video generation, lipsync generation and real-time synthetic media applications.
- Experience in cloud-based AI pipelines (AWS, Google Cloud, or Azure) and model compression techniques (quantization, pruning, distillation).
- Contributions to open-source projects or published research in AI-generated content, speech synthesis, or video synthesis.
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