Data/Machine Learning Engineer, Caresyntax

• 2-3 years of relevant or transferable experience (e.g., Data/Ops, Computer Vision/Machine Learning, CI/CD including GitLab, and DevOps/MLOps infrastructure/pipelines)
• Comfortable with modern, cloud-native computing paradigms such as the Docker/Kubernetes ecosystem on AWS or a similar cloud provider
• Familiarity with Terraform IaC for infrastructure management
• Familiarity with data-oriented workflow orchestration frameworks like Airflow
• Familiarity with the architecture and operation of large language models, and a clear understanding of their capabilities and limitations
• Thorough understanding of Generative AI architectures (e.g., Retrieval Augmented Generation RAG)
• Experience in development of Linux systems
• Strong coding skills in Python
• Healthcare experience is a significant plus
• Excellent communication, creativity, and problem-solving skills
• 1+ years experience building production-quality software
• Bachelors or Master’s degree and/or equivalent professional experience

Desirable

• We’re looking for a Data/ML Engineer to join our Computer Vision team, focusing on building end-to-end Data pipelines, MLOps, and integrating large language models (LLMs)
• This critical role involves participating in the design, development, and maintenance of our AI solutions to provide high-performance experiences for end users, such as surgeons
• You will work closely with cross-functional teams to ensure our solutions are scalable, efficient, and effective
• Infrastructure Development & Management: Design, build, and scale robust infrastructure using Terraform IasC for efficient machine learning model deployment, including orchestration, version control, and monitoring systems to ensure reliability and performance
• Automation & CI/CD Integration: Develop automation pipelines for model lifecycle management and integrate CI/CD practices for consistent model delivery
• Production Deployment & Monitoring: Deploy machine learning models into production environments and establish monitoring and alerting systems. Track model performance, health, and data drift while proactively troubleshooting issues and optimizing model efficiency
• Data Collection & Preparation: Gather and preprocess data from diverse sources, including structured data from data lakes, transactional databases, Electronic Health Records (EHR), as well as unstructured data from videos, images, and PDFs
• MLOps Infrastructure: Design and manage MLOps infrastructure on cloud platforms such as AWS and GCP to support scalable and efficient machine learning operations
• Generative AI & Text Processing: Develop and refine prompts for large language models (LLMs) and implement advanced text processing and retrieval-augmented generation (RAG) techniques. Focus on generating, summarizing, and analyzing text, images, and video to ensure high-quality, relevant, and contextually accurate outputs
• Code Review & Quality Assurance: Conduct thorough code reviews to maintain high standards of code quality and ensure adherence to best practices in software development