Job description

We're seeking a dynamic and innovative DevOps Engineer for AI Applications who thrives on turning complex challenges into seamless solutions and is passionate about driving the future of AI in production environments. If you love creating cutting-edge products and pioneering new features, join our team to revolutionize how machine learning models are deployed, monitored, and maintained at scale.

Product:
A solution created to automate support (text communication) for popular CRM systems using ML. The product is currently in production.

Team:
We already have an experienced CEO & CTO with a background in Data Science and Machine Learning, Front-End, Back-end developers, Machine Learning Engineer, Marketing Manager, and Designer.

Together we will:

Deploy and maintain AI applications in production environments;
Design and implement CI/CD pipelines to automate deployment processes;
Monitor system performance and implement optimizations as needed;
Collaborate with ML engineers and data scientists to integrate AI models seamlessly;
Manage infrastructure using containerization and orchestration tools;
Ensure security and compliance standards are met in all deployments.

Requirements

What you will need:

2.5-3 years of commercial experience in a related role (e.g., DevOps Engineer, Infrastructure Engineer), preferably with experience in AI application deployment;
Hands-on experience in deploying and maintaining AI applications in production environments;
Proficiency with containerization technologies, such as Docker, and orchestration tools like Kubernetes;
Experience with monitoring and logging tools, such as Prometheus, Grafana, or the ELK Stack;
Strong knowledge of CI/CD pipelines and tools, like Jenkins or GitLab CI/CD, to automate deployment processes;
Experience with at least one major cloud platform, such as AWS, Azure, or Google Cloud;
Solid scripting skills using languages like Bash and Python for automation tasks;
Understanding of infrastructure as code (IaC) tools, such as Terraform or CloudFormation;
Familiarity with configuration management tools, like Ansible, Chef, or Puppet;
Proficiency in Git and understanding of branching, merging, and pull requests;
Basic understanding of MLOps practices and tools, such as MLflow or Kubeflow, is a plus;
Experience with cloud-based ML services, like AWS SageMaker, Azure ML, or Google AI Platform, is advantageous;
Strong problem-solving skills and ability to work collaboratively in a team environment.

Would be a plus:

Experience with cloud-based ML services, like AWS SageMaker, Azure ML, or Google AI Platform, is advantageous.
Knowledge of model interpretability and explainability techniques;
Exposure to data engineering concepts and tools (e.g., ETL processes, data warehousing);
Good sense of humor 😊

Benefits

We offer: