MLOps Engineer

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.

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.