Machine Learning Engineer (Full-Time)

YOUR PROFILE

• Strong problem-solving and analytical skills, with the ability to think critically and creatively about complex challenges
• Excellent communication and collaboration skills, with the ability to work effectively with cross-functional teams and stakeholders at all levels of the organization
• Ability to manage personal workloads efficiently, prioritize tasks, manage timelines, and deliver high-quality results on schedule
• A mindset focused on continuous learning, with a passion for staying up to date on the latest advancements in machine learning and artificial intelligence
• Attention to detail and a commitment to producing high-quality, reliable, and maintainable code
• Bachelor's or Master's degree in data science, computer science, mathematics, statistics, or a related field
• Strong programming skills in Python, with experience in machine learning libraries and deep learning frameworks
• Proven experience working with Large Language Models (LLMs)
• A solid understanding of AI agents and agentic workflows, LLM orchestration frameworks, and related patterns
• Experience with data preprocessing, feature engineering, and model selection and evaluation techniques
• Knowledge of statistical and mathematical concepts relevant to machine learning, such as probability, linear algebra, and optimization
• Understanding best practices in software development, including version control, testing, and design, and implementing new systems or projects

Tasks

• Support and integrate AI models (e.g., LLMs) to ensure optimal performance and reliability
• Optimize agent-based workflows and AI agents for production use cases
• Testing and optimizing systems through tests to ensure accurate, consistent, and safe outputs
• Performance evaluation of continuous model testing and improving them over time
• Collaborate with platform teams, data scientists, and other stakeholders to integrate AI/ML solutions into existing systems and processes
• Participate in code reviews, testing, and debugging to ensure the quality and reliability of machine learning solutions