Accountabilities:

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

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

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

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

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

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

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

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

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

Requirements

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

Python proficiency: Strong hands-on programming skills in Python, with the ability to develop, debug, and maintain machine learning applications.

Machine learning frameworks: Practical experience with PyTorch or comparable machine learning frameworks.

LLM expertise: Experience working with Hugging Face Transformers and modern Large Language Models.

Model architecture knowledge: Strong understanding of transformer-based architectures and the principles behind modern machine learning systems.

Fine-tuning and inference: Practical knowledge of model fine-tuning, inference workflows, and techniques for adapting models to specific requirements.

Evaluation capabilities: Familiarity with model evaluation methodologies and the ability to assess accuracy, performance, and output quality.

Prompt engineering: Understanding of prompt design and optimization techniques for improving LLM behaviour and task-specific results.

Codebase navigation: Ability to independently read, understand, navigate, and modify existing machine learning codebases.

Version control: Experience using Git and GitHub for collaborative development, code management, and maintaining software quality.

Analytical thinking: Strong debugging, critical thinking, and problem-solving abilities, with attention to technical detail.

Independent working style: Ability to manage assigned tasks autonomously, investigate unfamiliar implementations, and deliver reliable technical improvements.

Benefits

Competitive hourly compensation: Earn between USD 30 and USD 80 per hour, depending on experience, skills, and technical expertise.

Flexible part-time commitment: Approximately 10–15 hours per week, allowing you to balance this opportunity with other professional or personal commitments.

Fully remote work: Work from anywhere in India without location-based office requirements.

Advanced AI exposure: Contribute to projects involving modern machine learning systems, LLMs, and transformer-based architectures.

Practical engineering experience: Apply your technical expertise to real-world model development, fine-tuning, debugging, and evaluation challenges.

Professional development: Strengthen your experience with cutting-edge AI technologies, tools, and machine learning methodologies.

Meaningful contribution: Support initiatives focused on improving the development, quality, and evaluation of advanced AI systems.

How Jobgether works

We use an AI-powered matching process to ensure your application is reviewed quickly, objectively, and fairly against the role's core requirements. Our system identifies the top-fitting candidates, and this shortlist is then shared directly with the hiring company. The final decision and next steps (interviews, assessments) are managed by their internal team.

We appreciate your interest and wish you the best!

Why Apply Through Jobgether?

Data Privacy Notice: By submitting your application, you acknowledge that Jobgether will process your personal data to evaluate your candidacy and share relevant information with the hiring employer. This processing is based on legitimate interest and pre-contractual measures under applicable data protection laws (including GDPR). You may exercise your rights (access, rectification, erasure, objection) at any time.

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