AI Research Scientist - Model Finetuning & Evaluation

Requirements:
Technical Skills:
o Machine Learning Frameworks: Advanced expertise in TensorFlow,PyTorch, and Hugging Face for developing and fine-tuning machine learning models.
o Cloud Platforms: Proficient in using AWS Sagemaker for model training, deployment, and management, including experience with distributed training and large-scale model deployment.
o Model Evaluation Tools: Hands-on experience with MLFlow for tracking experiments, hyperparameter tuning, and managing model lifecycles.
Proficiency in TensorBoard for visualizing model performance metrics.
Data Engineering: Strong skills in data preprocessing, feature engineering, and creating custom datasets. Ability to work with both structured (e.g., tabular data) and unstructured data (e.g., text, images, audio) within data lakes, and experience with ETL processes.
o Optimization Techniques: Familiarity with model optimization techniques such as quantization, pruning, and knowledge of accelerating model training through techniques like mixed-precision training and distributed computing.

Programming Languages:
o Python: Advanced proficiency with Python for implementing machine learning models, scripting, and automation.
o R: Experience with R for statistical modeling and data analysis

Experience:
o Model Development: Proven experience in developing, fine-tuning, and deploying large-scale machine learning models, particularly in NLP, computer vision, and audio processing.
o Research & Innovation: Experience in conducting research on cutting-edge AI/ML methodologies, with a track record of contributing to the development of new techniques or models.
o Model Lifecycle Management: Demonstrated ability in managing the entire lifecycle of machine learning models, from training and evaluation to deployment and monitoring, including handling model versioning and continuous learning (RLHF).
o Data Handling: Experience in working with large-scale datasets, managing data pipelines, and ensuring data quality and relevance for training purposes.

Education:
o Academic Qualifications: Masters or PhD in AI, Machine Learning, Computer Science, or related fields, with a strong academic record and research focus.
o Certifications: Relevant certifications in machine learning, cloud computing, or data science would be advantageous.

Other Skills:
o Problem-Solving: Strong analytical skills with the ability to troubleshoot and resolve complex infrastructure and deployment issues in a timely manner.
o Collaboration: Ability to work closely with AI Team and developers to understand infrastructure needs and provide robust technical support.
Documentation: Experience in creating clear, detailed documentation for infrastructure setups, deployment pipelines, and operational procedures to facilitate knowledge sharing and reproducibility