Machine Learning Engineer

Job description

We are looking for an experienced Machine Learning Engineer to join our team. In this role, you will play a key role in developing, deploying, and maintaining robust, scalable AI solutions in a DevOps environment. You will contribute throughout the entire lifecycle—from conception to deployment.

Tasks

- Develop, deploy, and optimize machine learning and AI solutions to tackle complex business challenges. - Build multi-agent systems and equip AI models with the ability to call functions and tools. - Design and implement RAG systems to ground AI responses in enterprise data. - Evaluate and integrate AI models (e.g., LLMs) to ensure optimal performance and reliability. - Optimize agent-based workflows and AI agents for production use cases. - Test and optimize system prompts and few-shot examples to ensure accurate, consistent, and safe AI outputs. - Conduct performance evaluations of machine learning and AI models using appropriate metrics, evaluation sets, and techniques, and continuously iterate to improve them. - Collaborate with platform teams, data engineers, data scientists, and other stakeholders to integrate machine learning solutions into existing systems and processes. - Participate in code reviews, testing, and debugging to ensure the quality and reliability of machine learning solutions.

Requirements

- 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 effectively manage personal workflows, prioritize tasks, manage schedules, and deliver high-quality results on time. - A commitment to continuous learning, with a passion for staying up to date on the latest developments in machine learning and artificial intelligence. - Attention to detail and a commitment to writing high-quality, reliable, and maintainable code. - Experience with large language models (LLMs). - A solid understanding of AI agents and agent-based 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 of 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.

We offer

No information available.