At Cardo AI, we are on a mission to build the best technology empowered by AI algorithms for private market investments.

Our engineers don’t just make things – we make things possible. We provide infrastructure, speed and precision to institutional investors, banks and credit originators in solving the most challenging and complex credit investment problems.

We are looking for a motivated Associate MLOps Engineer to join our Data Science team and support Cardo AI’s rapid growth.

Typically you have high sensibility on engineering the whole ML workflow from development to productionazation. You understand the MLOps iteration cycles and you’re familiar with the associated concepts (e.g. CI/CD/CT loops). You’ll be part of the Data Science Team MLOps competence center where you’ll have the chance to support our Data Scientists and Machine Learning Engineers in delivering to the market innovative products.

As an Associate MLOps Engineer in CardoAI you will:

• Design and maintain ML development infrastructure
• Support Data Scientists in the productionization of trained models
• Develop and deploy scalable tools and services for our internal clients to handle machine learning training and inference
• Be a reference for data scientists since the early stages of a project for identifying proper architectures.
• Identify and evaluate new technologies to improve the performance, maintainability, and reliability of our internal clients’ machine learning systems
• Act as the point person for Cloud Engineering matters within the team

• Cloud Engineering:
• IaaS (Infrastructure As A Service) principles in Cloud Computing Environments
• Basics of networking in Cloud Environments
• Application of previous knowledge in at least one of the three main public cloud providers (AWS, GCP or Azure)
• Basics of Kubernetes (CKAD certification or equivalent knowledge)
• Basics of Infrastructure As A Code with at least basics knowledge of Terraform or equivalent
• Software Engineering:
• Basics of PYTHON
• Version control through GIT
• Proficiency in BASH
• Machine learning / MLOps technologies:
• Knowledge of main Deep Learning frameworks (Tensorflow, PyTorch, Keras) with focus on infrastructure-related principles (e.g GPU integration)
• Deep knowledge of containerization technologies: Docker or equivalent
• Knowledge of MLOps principles (e.g the CI/CD/CT framework)
• Understanding of Model serving infrastructure principles (serveless scaling, multi tenant serving, etc.)
• Data Engineering:
• Basics of relational data models
• Knowledge of object storage design and technologies (e.g., AWS s3)

• Software Engineering:
• GitOps principles and technologies (ARGO CD, Git pipelines, etc.)
• Machine learning / MLOps technologies:
• Knowledge of MLOps frameworks (e.g Kubeflow, Google Vertex, Seldon stack, etc.)
• Understanding of Model tracking (e.g MLFlow, Tensorboard, etc.) and monitoring (e.g., Prometheus) tools
• Data Engineering:
• Basic knowledge of semi-structured data models (e.g., Elasticsearch)
• Basic knowledge of APACHE SPARK design and principles