Our client is a tech-driven beauty brand based in NYC & Tel-Aviv and backed by the world‘s largest consumer-focused PE fund. Through their technology and best-in-class products combining the worlds of beauty and AI, in just 2 years they have disrupted the industry by shifting millions of customers to shopping for beauty online. They are bold in their approach, ready to invest behind their capabilities, and strongly support experimentation and intellectual freedom to push the boundaries to which technology and data can take us. As a result of the first-of-its-kind, tech-driven approach and data capabilities, they are the fastest-growing beauty brand in the world.

They have more than 250 employees, in NYC, Tel-Aviv, Ukraine, and Serbia. The tech team in Kyiv includes 40 smart & talented Software Engineers & QAs. They are currently expanding the rapidly growing team and looking for bright and hungry individuals who want to have an essential role and join the team of a disruptive startup.

We are seeking a skilled and motivated DevOps Engineer with a strong MLOps background. You will play a pivotal role in integrating and optimizing machine learning workflows into the DevOps pipeline. This position is ideal for someone with a passion for automation, scalability, and operational excellence in AI/ML systems.

Key Responsibilities:

Infrastructure & Automation
- Design, implement, and maintain scalable CI/CD pipelines tailored for AI/ML projects. 
- Automate the deployment and monitoring of ML models in production using tools like Kubernetes, Docker, or similar containerization platforms.

MLOps Integration
- Develop workflows for continuous model training, validation, and deployment.
- Collaborate with Data Scientists to ensure seamless integration of models into production environments.
- Implement versioning and tracking systems for datasets, models, and training pipelines (e.g., MLflow, DVC).

Monitoring & Optimization
- Build systems to monitor the performance of ML models in production and trigger retraining when necessary.
- Optimize resource utilization for training and inference workloads on cloud or hybrid environments.
Collaboration
- Partner with cross-functional teams, including Data Scientists, Engineers, and Product Managers, to align infrastructure goals with business needs.
- Document processes and educate team members on best practices for DevOps and MLOps.

Qualifications:

Technical Skills
- Proficiency in Python, Bash, or similar scripting languages.
- Experience with CI/CD tools (e.g., Jenkins, GitLab CI/CD, CircleCI).
- Strong understanding of containerization and orchestration tools (Docker, Kubernetes).
- Familiarity with cloud platforms (AWS, Azure, GCP) and related services for ML workloads.
- Hands-on experience with MLOps tools like Kubeflow, MLflow, DVC, or TensorFlow Serving.

Data/ML Expertise
- Knowledge of ML model lifecycle management.
- Experience with monitoring tools for models (e.g., Prometheus, Grafana, SageMaker Model Monitor).

Soft Skills
- Strong problem-solving skills and a proactive mindset.
- Excellent communication skills to collaborate with technical and non-technical stakeholders.
- Good spoken and written English communication skills.

Preferred Qualifications:

- Experience with large-scale distributed systems.
- Understanding of security best practices in cloud environments.
- Certification in cloud platforms (AWS/GCP/Azure) or DevOps.
- Familiarity with tools like Apache Airflow, Argo Workflows, or Terraform.

Benefits and working conditions:
- Competitive salary and benefits package.
- Opportunity to work on cutting-edge AI/ML projects.
- A collaborative and innovative work environment.
- Professional growth and development opportunities.