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2 days ago

rajahmundry, India beBeeDataScientist Full time

Data ScientistWe're seeking a highly skilled professional with 4–10 years of experience in applied AI/ML to join our fast-paced environment. This role requires deep expertise in transformer architectures and strong fundamentals in model training, fine-tuning, and optimization.The ideal candidate thrives in a dynamic, high-velocity R&D setting, 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.Key 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.BenefitsCompetitive Salary: We offer a competitive salary package.Proximity Talks: Meet other designers, engineers, and product geeks – and learn from experts in the field.Keep on learning with a world-class team: Work with the best in the field, challenge yourself constantly, and learn something new every day.

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