Description

Design, build, and deploy large-scale generative AI pipelines and LLM-driven synthetic data systems. You will own the end-to-end lifecycle from data engineering to model fine-tuning and inference.

This role is on-site.

Responsibilities

• Design and implement LLM-driven synthetic data pipelines for high-quality training datasets.
• Build and deploy generative AI models, including diffusion models and LLMs, for advanced ML use cases.
• Fine-tune and optimize large language models for specific downstream tasks and performance gains.
• Develop scalable data pipelines supporting training, evaluation, and inference workflows.
• Conduct exploratory data analysis to identify model enhancements and dataset improvements.

Required Skills

• Bachelor’s degree in Computer Science or a related technical field.
• 5+ years of experience in machine learning or software engineering.
• Expert-level proficiency in Python.
• Strong hands-on experience with PyTorch and deep learning frameworks.
• Proven experience working with Large Language Models (LLMs).
• Experience building and deploying diffusion models.
• Strong analytical, debugging, and problem-solving skills.
• Ability to collaborate with researchers and engineers to define technical requirements.

Preferred Skills

• Experience with large-scale data pipeline architecture.

Key Skills

Education

Any Graduate