Job Description

Core Data Science Skills:

• Expert-level proficiency in statistical methods, hypothesis testing, and experimental design
• Deep understanding of machine learning algorithms, including supervised, unsupervised, and reinforcement learning
• Strong foundation in probability theory, linear algebra, and mathematical optimization

Generative AI Specialization:

• Proven experience with Large Language Models (LLMs) including GPT, BERT, T5, and similar architectures
• Hands-on experience with audio synthesis technologies (e.g., WaveNet, Tacotron, VALL-E)
• Expertise in image generation models (e.g., Stable Diffusion, DALL-E, GANs, VAEs)
• Experience with video generation and manipulation techniques
• Working knowledge of multimodal AI systems and cross-modal learning

Technical Implementation:

• Proficiency in Python and relevant ML libraries (PyTorch, TensorFlow, Hugging Face, scikit-learn)
• Experience with MLOps tools and practices (MLflow, Weights & Biases, Kubeflow)
• Knowledge of cloud platforms (AWS, GCP, Azure) and containerization (Docker, Kubernetes)
• Familiarity with distributed computing frameworks (Ray, Dask, Spark)

Evaluation & Benchmarking:

• Expert knowledge in AI model evaluation methodologies and metrics such as Ragas
• Experience with benchmark datasets and evaluation frameworks (BLEU, ROUGE, FID, IS, CLIP Score)
• Proficiency in statistical analysis and performance measurement tools
• Understanding of bias detection and fairness evaluation in AI systems

AI Solution Development

• Design, prototype, and implement generative AI solutions across text, audio, image, and video modalities
• Develop and optimize multimodal RAG (Retrieval-Augmented Generation) systems for complex use cases
• Build end-to-end pipelines for training, fine-tuning, and deploying large language models and other generative models
• Create scalable architectures for real-time and batch AI inference systems

Model Evaluation & Benchmarking

• Design comprehensive evaluation frameworks for generative AI models across different modalities
• Establish benchmarking protocols and metrics for model performance assessment
• Conduct A/B testing and statistical analysis to validate model improvements
• Develop automated testing pipelines for continuous model validation

Research & Innovation

• Stay current with latest developments in generative AI, LLMs, and multimodal systems
• Experiment with state-of-the-art models and techniques in audio synthesis, image generation, and video creation
• Contribute to technical publications, patents, and open-source projects
• Collaborate with research teams to translate cutting-edge research into practical applications

Technical Leadership

• Mentor junior data scientists and provide technical guidance on AI projects
• Establish best practices for AI model development, testing, and deployment
• Lead cross-functional collaboration with product, engineering, and business teams
• Drive technical decision-making for AI architecture and technology stack selection

• Bachelor's or Master's in Computer Science, Data Science, Machine Learning, Statistics, or related quantitative field
• Minimum 5+ years of hands-on experience in data science and machine learning