Machine Learning Engineer

About the role

This position is listed on behalf of a partner company, who manages all applications and next steps. Our partner is looking for a Machine Learning Engineer based in India.

As a Machine Learning Engineer, you will contribute to the development, improvement, and evaluation of modern machine learning systems, with a particular focus on Large Language Models (LLMs) and transformer-based architectures. You will work with existing machine learning codebases, analysing their structure and implementing practical enhancements to improve model performance and reliability. Using Python, PyTorch, and Hugging Face Transformers, you will support model fine-tuning, inference, and evaluation across a range of AI projects. You will also explore prompt engineering techniques and investigate model behaviour to identify opportunities for optimization. This role offers the opportunity to work on advanced AI initiatives involving real-world machine learning challenges. With a flexible, fully remote, part-time arrangement, you can contribute independently while collaborating on innovative AI development projects.

Accountabilities: -

Understand existing ML systems: Review and navigate established machine learning codebases to understand their architecture, functionality, dependencies, and implementation details.

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Develop and improve ML implementations: Write, modify, debug, and optimize machine learning solutions using Python and frameworks such as PyTorch.

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Work with modern AI architectures: Implement and support transformer-based models and LLM applications using Hugging Face Transformers and related technologies.

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Support model fine-tuning: Apply fine-tuning techniques to adapt existing models to specific tasks, improve their performance, and enhance output quality.

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Perform model inference and evaluation: Run experiments and evaluate model accuracy, performance, consistency, and overall effectiveness using appropriate evaluation methodologies.

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Apply prompt engineering: Design and refine prompts and explore model adaptation techniques to improve responses and task-specific behaviour.

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Troubleshoot technical challenges: Investigate model-related issues, debug implementation problems, identify performance gaps, and recommend practical improvements.

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Manage development workflows: Use Git and GitHub for version control, code collaboration, code reviews, and maintaining organized development processes.

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Document technical findings: Record implementation changes, evaluation results, technical decisions, and relevant findings to support project continuity and knowledge sharing.

Requirements:

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Professional experience: At least 2 years of experience in Machine Learning Engineering, Applied Machine Learning, AI Engineering, or a related technical discipline.