AI Research Scientist

3 weeks ago

Anywhere in IndiaMultiple LocationsGujaratAhmedabadRemote, IN Etech Global Services Full time

SALARY : 12LPA - role : AI Research Scientist - Model Finetuning & Evaluation


Job type : Remote

Shift : 8 PM to 5 AM

Experience : 2+ Years

Benefits : Food allowance, Night shift allowance (as per process), Internet allowance, Health insurance, and many more

Job Title :

- AI Research Scientist - Model Finetuning & Evaluation

Job Overview :

The AI Research Scientist will focus on advancing the performance and capabilities of AI models through research, finetuning, and comprehensive evaluation. This role emphasizes model development, evaluation, and optimization in areas such as NLP, audio processing, and computer vision. The scientist will align machine learning objectives with business goals and contribute to internal intellectual property through detailed research documentation.

Key Responsibilities :

Model Research & Development :

- Conduct research on cutting-edge AI and machine learning developments in the field.

- Develop and fine-tune models in NLP, computer vision, and audio processing

Deployment & Optimization :

- Deploy Hugging Face models, utilize AWS Sagemaker for model training, and implement optimization techniques like quantization.

- Create, manage and evaluate checkpoints during training.

- Deploy models using developed weights.

Model Evaluation & Monitoring :

- Use tools like MLFlow and TensorBoard for model evaluation and performance monitoring.

- Create Model Artifacts for audits and ability for deployment of versioned models.

- Monitor Models for model drift, degradation of results, and incorporating continuous RLHF.

Data Engineering & Training Data Preparation :

- Collaborate with AI-Team and developers to create high-quality training datasets and optimize data workflows.

- Able to work with both structured and unstructured data in a Datalake for creating unique training datasets.

Research & Documentation :

- Develop internal research papers and documentation to enhance the company's proprietary capabilities.

- Create reports for potential capabilities of researched modeling capabilities and new features.

Collaboration & Alignment :

- Align ML objectives with business goals and work closely with other AI team members to ensure integrated solutions.

Requirements :

Technical Skills :

- Machine Learning Frameworks : Advanced expertise in TensorFlow, PyTorch, and Hugging Face for developing and fine-tuning machine learning models.


- Cloud Platforms : Proficient in using AWS Sagemaker for model training, deployment, and management, including experience with distributed training and large-scale model deployment.

- 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.

- 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 :

- Python : Advanced proficiency with Python for implementing machine learning models, scripting, and automation.

- R : Experience with R for statistical modeling and data analysis.

- Experience :

- Model Development : Proven experience in developing, fine-tuning, and deploying large-scale machine learning models, particularly in NLP, computer vision, and audio processing.

- 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.

- 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).

- Data Handling : Experience in working with large-scale datasets, managing data pipelines, and ensuring data quality and relevance for training purposes.

(ref:hirist.tech)

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