Company Description

LVIS, through our advanced brain network analysis tools, provides Live Visualization to innovate how neurological diseases are diagnosed and treated. LVIS is a leader in cutting edge neural information analysis technologies that can decode brain networks and find cures for neurological diseases. LVIS owns patented technologies and our team includes leaders with strong expertise in neuroscience and engineering from Stanford University. LVIS has been selected to be a member of the Stanford StartX community and the NVIDIA inception program. We have an international team with our headquarter located in Palo Alto, California, USA and we have an office in Gangnam, Seoul, South Korea. We are looking for talented individuals to join us in transforming the neurology health care industry.

LVIS provides an environment where everyone is expected to grow with the company. We are looking for self motivated team members who will be at the leading edge of technology.

Responsibilities

Develop and maintain APIs: Deploy machine learning models as APIs and build scalable API services.

Optimize machine learning workloads on the cloud: Improve performance and cost-efficiency of ML models on AWS.

Ensure operational stability: Design and implement real-time data processing, logging, monitoring, and model performance tracking systems.

Deploy ML models in real-world environments: Ensure reliable deployment and management of ML models and data drift in production.

Utilize GPU and GPU clusters: Configure and optimize GPU-based environments for model training and inference (on Cloud and on-premise).

Build MLOps pipelines on AWS and Kubernetes: Deploy and manage containerized ML workflows in Kubernetes environments.

Basic Qualifications

Bachelor’s degree or higher in Computer Science, Data Science, AI, or related fields.

5+ years of experience in MLOps or ML Engineering.

Experience of ML related programming and frameworks (Python, PyTorch, TensorFlow, etc.)

Experience deploying and managing ML models using tools like MLflow, TensorFlow Serving, or TorchServe.

Hands-on experience with AWS.

Proficiency in managing and orchestrating containers using Kubernetes and Docker.

Experience with GPU-based model training and optimization.

Experience building large-scale data processing pipelines (Airflow, Kafka, etc.).

Qualifications

Hands-on experience with CI/CD pipelines (Jenkins, ArgoCD, etc.).

Experience in large-scale AI model serving.

Knowledge of real-time data streaming and batch processing.

Preferably DevOps experience along with MLOps.