Design and implement end-to-end machine learning solutions, from data preprocessing to model deployment and monitoring
Develop and optimize agentic Large Language Models (LLMs) solutions for healthcare applications using techniques like fine-tuning and Retrieval-Augmented Generation (RAG)
Create robust data pipelines for validation, and deployment
Implement ML systems that can effectively process and analyze diverse healthcare data types, including structured clinical data, medical imaging, and unstructured text
Collaborate with backend engineers to integrate ML models into our production infrastructure
Ensure ML systems meet strict healthcare compliance requirements while maintaining high performance
Design and implement monitoring systems for model performance, data drift, and system health
Lead research initiatives to explore and implement state-of-the-art AI/ML techniques
Collaborate with clinical experts to validate model outputs and ensure medical accuracy
Stay current with the latest developments in AI/ML, particularly in healthcare applications

5+ years of experience in machine learning engineering, with a focus on production ML systems
Strong proficiency in Python and AI/LLM frameworks (LangChain, LangGraph, AutoGen, CrewAI)
Experience designing and implementing production-grade ML pipelines
Proven track record of deploying ML/LLM solutions in production environments
Strong understanding of ML fundamentals, including deep learning, NLP, and statistical modeling
Experience with ML ops tools and best practices
Proficiency in working with large-scale datasets and distributed computing
Strong software engineering skills and coding best practices
Degree in Computer Science or equivalent degree.

AI/Machine Learning Engineer

Key skills