MLOps Engineer

We're seeking a dynamic and innovative ML Ops Engineer 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:

• Assist in building and maintaining ML infrastructure and pipelines;
• Support the deployment and monitoring of ML models in production;
• Collaborate with data scientists and software engineers to ensure smooth integration of ML models;
• Implement and maintain CI/CD pipelines for ML projects;
• Troubleshoot issues related to ML model performance and deployment;
• Contribute to the documentation and best practices for MLOps processes;

Requirements

What you will need:

• 1-1.5 years of commercial experience in a related role (e.g., ML Engineer, Data Engineer, DevOps Engineer);
• Hands-on experience in deploying and maintaining machine learning models in production;
• Experience in monitoring and logging tools (e.g., Prometheus, Grafana, ELK Stack);
• Experience with popular Machine Learning frameworks and libraries (e.g., TensorFlow, PyTorch, scikit-learn);
• Solid experience with Python;
• Experience with scripting languages (e.g., Bash);
• Experience with at least one major cloud platform (AWS, Azure, Google Cloud);
• Basic understanding of MLOps practices and tools (e.g., MLflow, Kubeflow);
• Basic knowledge of CI/CD pipelines and tools (e.g., Jenkins, GitLab CI);
• Understanding of containerization technologies (e.g., Docker) and orchestration tools (e.g., Kubernetes);
• Familiarity with cloud-based ML services (e.g., AWS SageMaker, Azure ML, Google AI Platform);
• Proficiency in using Git and understanding of branching, merging, and pull requests.

Would be a plus:

• Experience with distributed computing frameworks (e.g., Apache Spark);
• Knowledge of model interpretability and explainability techniques;
• Familiarity with automated testing and quality assurance practices for ML models;
• Exposure to data engineering concepts and tools (e.g., ETL processes, data warehousing);
• Good sense of humor 😊

Benefits

We offer:

• Compensation in USD;
• Remote work in Ukraine or in a similar time zone;
• Adequate, friendly management and no bureaucracy;
• Cozy startup atmosphere with stability from a holding company;
• Plenty of interesting talks and communication with the team.