Machine Learning Engineer

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.