- Create multi-agent systems and equip AI models with function/tool-calling capabilities
- Design and implement RAG systems to ground AI responses in enterprise data
- Assess and integrate AI models (e.g. LLMs)ensuring optimal performance and reliability
- Optimize agentic workflows and AI agents for production use cases
- Testing and optimizing system prompts and few-shot example to ensure accurate, consistent, and safe AI outputs
- Performance evaluation of machine learning and AI models using appropriate metrics, evaluation sets and techniques, and continuously iterate and improve upon 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
- 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
At Matrix Automation, we use technology to streamline and automate tedious manual work and co-create novel concepts and business models to bring your imagination to life. We deliver on the era of Human-Computer Understanding.
Visit www.matrix-process.com for more information