MLOps - Model Deployment (Machine Learning Operations) Remote

MLOps - Model Deployment [Machine Learning Operations] US Remote and Mexico Remote - High-Level Objective: Take existing open-source Machine Learning (ML) models for chemical synthesis (predicting products from reactants) and retrosynthesis (determining the inverse route) and deploy them within Google Cloud Platform (GCP) to ensure they are accessible by dependent services. - Specific Tasks: The scope requires the deployment of several models, including, but not limited to: MolecularTransformer, ChemFormer, ReactionT5, RetroChimera (note that each model may present distinct challenges related to memory, architecture, and dependencies). This deployment process involves several steps for each model: - Creating a Dockerfile and building the corresponding Docker image. - Writing the necessary inference logic. - Deploying and testing the resulting endpoint on GCP. - Optimizing the deployment for latency. - Expected Outputs: Successful completion of the project will result in multiple ML models being made accessible to other services on GCP, deployed, for example, on a Vertex AI endpoint. - Required Skillset: The executing vendor must possess expertise in the following technologies: - GCP: Vertex AI, Artifact Registry - Tools/Frameworks: Docker, PyTorch, Hugging Face