Machine Learning Engineer (Full-Time) (f/m/d)

Tasks

• Development of multi-agent systems and integration of function/tool invocation capabilities into AI models
• Design and implementation of RAG systems to generate AI responses based on corporate data
• Evaluation and integration of AI models (e.g., LLMs) to ensure optimal performance and reliability
• Optimizing agent workflows and AI agents for production use cases
• Testing and optimizing system instructions and examples to ensure accurate, consistent, and safe AI outputs
• Evaluation of the performance of machine learning and AI models using appropriate metrics, evaluation datasets, and techniques, as well as continuous iteration and improvement
• 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
• A 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 with large language models (LLMs)
• A solid understanding of AI agents and agent workflows, LLM orchestration frameworks, and thought processes
• Experience with data preparation, feature engineering, and model selection and evaluation techniques
• Knowledge of statistical and mathematical concepts relevant to machine learning

We offer