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

Tasks

• Development of multi-agent systems and graphs with reasoning and vision capabilities
• Design and implementation of AI systems to embed AI responses in corporate data
• Evaluation and integration of AI models (e.g., LLMs) while ensuring optimal performance and reliability
• Optimization of Agent-Based Workflows and AI Agents for Production Use Cases
• Testing and optimizing system prompts and few-shot examples to ensure accurate, consistent, and safe AI outputs
• Evaluation 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 when faced with complex problems
• 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 efficiently manage multiple workloads, prioritize tasks, manage schedules, and deliver high-quality results on time
• A passion for continuous learning and an interest in staying up to date on the latest developments 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 such as TensorFlow or PyTorch
• Proven experience working with large language models (LLMs)
• A solid understanding of AI agents and agent-based workflows, LLM orchestration frameworks, and reasoning patterns
• Experience in data preprocessing, feature engineering, and techniques for model selection and evaluation
• Knowledge of statistical and mathematical concepts relevant to machine learning, such as probability theory, linear algebra, and optimization