Machine Learning Engineer Offline

Responsibilities

● Build the infrastructure for the ML lifecycle, from development to deployment and monitoring.

● Work together with Data Scientists, Data Engineers, Software Engineers, and Product teams to train, deploy, and manage ML models throughout their lifecycle - from development to production.

● Design, implement, manage, monitor, and optimize a scalable and robust infrastructure for machine learning workflows.

● Implement metrics-based processes to improve the accuracy and reliability of our ML models, including early detection and mitigation of performance issues.

● Implement and manage CI/CD pipelines for machine learning workflows.

● Automate model training, retraining, testing, validating, and deployment processes.

● Proactively identify and resolve issues related to model performance and data quality.

● Communicate effectively with stakeholders to understand requirements and provide updates on model deployment and performance.

Requirements

● 3+ years of hands-on experience as an MLOps or ML Engineer with ops orientation.

● Proven track record in building and managing ML pipelines, and CI/CD processes and tools.

● Extensive experience in ML workflows and Data Orchestration frameworks such as AirFlow, Prefect, MLFlow, Kubeflow, SageMaker, etc.

● Familiarity with container orchestration tools, including Kubernetes.

● Experience with AWS cloud-based services.

● Ability to write efficient, scalable Python code.

● Experience with source control (e.g., Bitbucket, Git).

● B.Sc. in Computer Science, Engineering, Math, or another quantitative field - an advantage.

● Strong problem-solving skills with good analysis for root cause detection.

● Ability to work both collaboratively with a team and independently.

● Self-learner with a can-do attitude.

We offer:

●flexible working schedule

●medical insurance

●free English classes

●working with latest cutting-edge technologies

●great career development opportunities in a growing company

●internal training in data engineering, data science