Generative AI Specialist

2 weeks ago

palakkad, India beBeeDataScience Full time

AI InnovatorWe're seeking an expert AI Innovator to join our fast-paced R&D environment. This role requires deep expertise in transformer architectures and strong fundamentals in model training, fine-tuning, and optimization.We'll work across modalities (text, audio, video), with the flexibility to specialize in one domain but the adaptability to experiment across others.The ideal candidate thrives in a startup-style, high-velocity environment, is execution-focused, and demonstrates ownership from architecture to deployment.You'll run rapid experiments, iterate on state-of-the-art models, and push the boundaries of generative AI in lip-sync, character consistency, audio realism, and video quality — with a research-first, problem-solving mindset.ResponsibilitiesModel Development & Fine-tuning: Run end-to-end experiments on transformer-based architectures (LLMs, Whisper, diffusion, LoRA, RLHF/SFT, multimodal models).Domain-Specific Applications:Audio: Lip-sync, emotional delivery (shouting, whispering, crying), regional language support.Video: Scene/character consistency, quality benchmarks comparable to Veo3/Sora.Text: Extend LLMs to handle regional languages and domain-specific adaptation.Evaluation & Optimization: Design automated evaluation frameworks for objective quality scoring (images, video frames, audio clips). Balance trade-offs in speed, quality, and efficiency.Cross-Modality Integration: Experiment with audio-video synchronization, background score integration, and text-to-video alignment.Research & Experimentation: Stay ahead of rapidly evolving models and tools, testing architectural variations and scaling solutions for production use.Ownership & Execution: Drive initiatives independently with strong problem-solving, accountability, and first-principles thinking.RequirementsExperience: 4–10 years in applied Data Science/ML with a strong focus on generative AI.Core Fundamentals: Solid grasp of transformer architectures, LLMs, training dynamics, and optimization techniques.Modality Depth: Expertise in at least one modality (text, audio, or video), with demonstrable end-to-end project experience.Hands-On Skills: Strong coding and debugging ability in Python, with deep learning frameworks (PyTorch, TensorFlow).Deployment Knowledge: Experience with ML pipelines (FastAPI or similar) for inference and deployment.Evaluation Metrics: Proven ability to design/implement automated evaluation methods for generative outputs.Adaptability: Ability to experiment quickly with new tools, libraries, and models in a dynamic environment.

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