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
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