ML Scientist - WGDT

Job Description

Key Responsibilities

Model Development :

Design, build, train, and optimize machine learning and deep learning models for real-world applications.

Cross-Functional Collaboration :

Work closely with product managers, data scientists, and software engineers to define AI / ML use cases and deliver high-impact solutions.

LLM Fine-Tuning :

Evaluate and fine-tune pre-trained Large Language Models (LLMs) for domain-specific tasks and performance improvements.

Model Deployment :

Deploy models into production using APIs, Docker containers, and cloud platforms such as AWS, GCP, or Azure.

Research & Innovation :

Stay updated with the latest AI / ML advancements, tools, and best practices to drive continuous innovation within the team.

Preferred Qualifications

Bachelor’s or Master’s degree in Computer Science, Data Science, AI, or a related field.

Strong experience with machine learning frameworks (TensorFlow, PyTorch, scikit-learn).

Proficiency in Python and relevant libraries (NumPy, Pandas, Transformers, etc.).

Experience deploying models in cloud environments.

Requirements

Key Responsibilities Model Development : Design, build, train, and optimize machine learning and deep learning models for real-world applications. Cross-Functional Collaboration : Work closely with product managers, data scientists, and software engineers to define AI / ML use cases and deliver high-impact solutions. LLM Fine-Tuning : Evaluate and fine-tune pre-trained Large Language Models (LLMs) for domain-specific tasks and performance improvements. Model Deployment : Deploy models into production using APIs, Docker containers, and cloud platforms such as AWS, GCP, or Azure. Research & Innovation : Stay updated with the latest AI / ML advancements, tools, and best practices to drive continuous innovation within the team. Preferred Qualifications Bachelor’s or Master’s degree in Computer Science, Data Science, AI, or a related field. Strong experience with machine learning frameworks (TensorFlow, PyTorch, scikit-learn). Proficiency in Python and relevant libraries (NumPy, Pandas, Transformers, etc.). Experience deploying models in cloud environments.