MLOps Engineer

About Neurodiscovery

Neurodiscovery is building an AI first OMICS platform that helps with research to discover cures for neurological conditions. The team is comprised of top neurologists, data scientists, data engineers and computational biologists to focus on finding cures for neurological conditions. We use large language models, knowledge graphs and topological methods to find unique patterns that lead to novel biomarkers, drug discovery and target identification. Our advisory board and investors include the top neurologists in the world, along with founders of multi-billion dollar health tech companies. You will get to work with a talented team.

Job Summary

The MLOps Engineer will be responsible for developing and maintaining the end-to-end machine learning lifecycle, from data collection and preparation to model training and deployment. The ideal candidate will have experience in managing the devops lifecycle of machine learning and deep learning projects, as well as hands-on experience in setting up large language models (LLMs).

Responsibilities

• Develop and maintain the end-to-end machine learning lifecycle on the data platform
• Develop, implement and manage MLOps in cloud infrastructure for data preparation, deployment, monitoring and retraining models
• Collaborate with the engineering team to ship and integrate machine learning models to our application at scale
• Experience with tools like Terraform, Jenkins, Docker, git
• Develop automation scripts and tools to streamline the MLOps processes, Implement version control and release management practices for machine learning assets.
• Monitor and troubleshoot LLMs, machine learning models and various ML pipelines
• Work with stakeholders to gather requirements and define success metrics
• Maintain comprehensive documentation for MLOps processes, workflows, and configurations.
• Stay up-to-date on the latest developments in machine learning, LLMs and DevOps

Requirements

• Bachelor's degree in computer science, engineering, or a related field
• 5+ years of experience in machine learning or deep learning, along with experience in building production level models
• Strong DevOps mentality: Knowledge of making a complicated pipeline simple and easy to maintain, with proven experience of Terraform, Jenkins, git
• Hands-on experience in setting up large language models (LLMs) such as Llama 2, Mistral or other open source models, along with basic understanding of Langchain and NLP
• Experience in deploying machine learning models to production, along with experience in monitoring and troubleshooting models in production
• Ability to think and work independently has an end-to-end solution mindset with demonstrated thought leadership in the ML Ops practice
• Exposure to deep learning approaches and modeling frameworks/libraries (PyTorch, Tensorflow, Keras, etc)
• Solid understanding and experience with cloud computing platforms such as AWS, Azure, or GCP
• Strong problem-solving and analytical skills
• Excellent communication and teamwork skills