28 Stable Diffusion Jobs - Page 2

Location: Gurugram (Work From Office) Job Type: Full-Time Job Description: 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: 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 . Qualifications: 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 . Preferred Qualifications: 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 .

Experience in ComfyUI (node-based AI workflow builder) — ability to build, modify, and optimize custom pipelines.Runway, Stable Diffusion, Midjourney, DALLE, Leonardo.ai, and other generative platforms.Video generation experience ( VFX).

We are looking for a GenAI Leader with 12+ years of experience and a strong background in pharma/life sciences to join our team. As a key leader, you will drive innovation in the pharma commercial and clinical space by designing cutting-edge AI solutions, leading high-performing teams, and delivering impactful results. If you have a proven track record in Generative AI, hands-on experience with models like GPT, DALL-E, and Stable Diffusion, and a passion for mentoring and thought leadership, we want to hear from you! Must have skills & competencies Enhance the go-to-market strategy by designing new and relevant solution frameworks to accelerate our clients journeys for impacting patient outcomes. Pitch for these opportunities and craft winning proposals to grow the Data Science Practice. Build and lead a team of data scientists and analysts, fostering a collaborative and innovative environment. Oversee the design and delivery of the models, ensuring projects are completed on time and meet business objectives. Engaging in consultative selling with clients to grow/deliver business. Develop and operationalize scalable processes to deliver on large & complex client engagements. Proven experience in building and productizing GenAI app at an enterprise level Ensure profitable delivery and great customer experience design the end-to-end solution, put together the right team and help them deliver as per established processes. Build an A team hire the required skills sets and nurture them in a supporting environment to develop strong delivery leaders for the Data Science Practice. Train and mentor staff and establish best practices and ways of working to enhance data science capabilities at Axtria. Operationalize an eco-system for continuous learning & development. Write white papers, collaborate with academia and participate in relevant speaker opportunities to continuously upgrade learning & establish Axtria's thought leadership in this space. Research, develop, evaluate and optimize newly emerging algorithms and technologies for relevant use cases in pharma commercial & clinical space. Extensive hands-on experience with Python, R, or Julia, focusing on data science and generative AI frameworks. Expertise in working with generative models such as GPT, DALL-E, Stable Diffusion, Codex, and MidJourney for various applications. Proficiency in fine-tuning and deploying generative models using libraries like Hugging Face Transformers, Diffusers, or PyTorch Lightning. Strong understanding of generative techniques, including GANs, VAEs, diffusion models, and autoregressive models. Experience in prompt engineering, zero-shot, and few-shot learning for optimizing generative AI outputs across different use cases. Expertise in managing generative AI data pipelines, including preprocessing large-scale multimodal datasets for text, image, or code generation. Experience in leveraging APIs and SDKs for generative AI services from OpenAI, Anthropic, Cohere, and Google AI. Expertise in defining generative AI strategies, aligning them with business objectives, and implementing solutions for scalable deployment. Knowledge of real-world applications of generative AI, such as text generation, code generation, image generation, chatbots, and content creation. Awareness of ethical considerations in generative AI, including bias mitigation, data privacy, and safe deployment practices. Good to have skills & competencies Proven ability to collaborate with cross-functional teams, including product managers, data scientists, and DevOps engineers, to deliver end-to-end generative AI solutions. Proven experience in leading teams focused on generative AI projects, mentoring ML engineers, and driving innovation in AI applications. Possessing robust analytical skills to address and model intricate business needs is highly advantageous, especially for those with a background in life sciences or pharmaceuticals.