Machine Learning Engineer 100%

Role overview

Responsibilities

• check_circle Develop, deploy, and optimize machine learning and AI solutions that address complex business challenges.
• check_circle Create multi-agent systems and equip AI models with function/tool-calling capabilities
• check_circle Design and implement RAG systems to ground AI responses in enterprise data
• check_circle Assess and integrate AI models (e.g. LLMs) ensuring optimal performance and reliability
• check_circle Optimize agentic workflows and AI agents for production use cases
• check_circle Testing and optimizing system prompts and few-shot example to ensure accurate , consistent, and safe AI outputs
• check_circle Performance evaluation of machine learning and AI models using appropriate metrics, evaluation sets and techniques, and continuously iterate and improve upon them
• check_circle Collaborate with platform teams, data engineers, data scientists, and other stakeholders to integrate machine learning solutions into existing systems and processes
• check_circle Participate in code reviews, testing, and debugging to ensure the quality and reliability of machine learning solutions

Basic qualifications

• 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 effectively, to prioritize tasks, manage timelines, and deliver high-quality results on schedule
• Continuous learning mindset, with a passion for staying up-to-date with the latest advancements in machine learning and artificial intelligence
• Attention to detail and 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 such as TensorFlow or PyTorch
• Proven experience working with Large Language Models (LLMs)
• Good understanding of AI agents & agentic workflows, LLM orchestration frameworks and reasoning 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 software development best practices, including version control, testing, and documentation
• Excellent problem-solving and debugging skills, with the ability to identify and resolve issues quickly and effectively
• Relevant work experience in machine learning, data science or a related field