Senior Machine Learning Engineer

The customer provides primary care and obesity medicine with a focus on compassionate, non-judgmental care. We are providing the service of the engineering team which is responsible for developing applications for the needs of the clinic and patients. The main focus of this job is to develop cross-platform (mobile and web) applications for patients. The development started very recently, and there are a lot of features waiting to be developed.
We are seeking a highly skilled and experienced Machine Learning Engineer to join our dynamic team. The ideal candidate will have a strong background in machine learning, particularly in Retrieval Augmented Generation (RAG) and Large Language Models (LLMs). Proficiency in JavaScript, API development, data engineering, and tools such as SQL and AWS Healthlake is essential. This role involves fine-tuning models, conducting prompt engineering, and utilizing a broad range of tools in Python and other programming languages.

• Bachelor’s or Master’s degree in Computer Science, Engineering, or a related field
• Proven experience in machine learning, specifically with RAG and LLMs
• Strong programming skills in Python
• Experience in API development and integration
• Proficiency in SQL and cloud-based data management solutions
• Knowledge of tools such as Guardrails, Lakera, and other relevant libraries
• Excellent problem-solving skills and the ability to work in a fast-paced environment
• Strong communication and teamwork abilities

Nice-to-Have:

• Programming skills in JavaScript
• Experience with AWS Healthlake

• Machine Learning & Model Development:

• Implement large language models with a focus on Retrieval Augmented Generation (RAG).
• Fine-tune and optimize models to enhance performance and accuracy
• Conduct prompt engineering to improve model outputs and user interaction
• Deploy and maintain vector database infrastructure for hosting large document chunks

• Software Development & API Integration:

• Design, develop, and maintain APIs for seamless integration of machine learning models into applications
• Collaborate with frontend and backend developers to ensure smooth implementation of features
• UClize JavaScript and other programming languages to build robust and scalable applications

• Database and Data Engineering:

• Deploy scalable, secure infrastructure for our RAG pipeline on AWS
• Manage and preprocess large documents for model fine-tuning and evaluation
• Use SQL for data manipulation, extraction, and analysis
• Work with AWS Healthlake and other cloud-based data storage solutions to efficiently manage healthcare data

• Tool Utilization & Collaboration:
  • Utilize a variety of tools such as Guardrails, Lakera, and other Python-based libraries to enhance model development and deployment
  • Collaborate with cross-functional teams to identify and address technical challenges
  • Stay updated with the latest advancements in machine learning and healthcare technology