ML Ops Engineer (on-site)

OPSWAT, a global leader in IT, OT, and ICS critical infrastructure cybersecurity, delivers an end-to-end platform that gives public and private sector organizations and enterprises the critical advantage needed to protect their complex networks, secure their devices, and ensure compliance. Over the last 20 years our commitment to innovative technology has earned the trust of more than 1,700 organizations, governments, and institutions globally, solidifying our role in protecting the world’s critical infrastructure and securing our way of life.

OPSWAT, a global leader in IT, OT, and ICS critical infrastructure cybersecurity, delivers an end-to-end platform that gives public and private sector organizations and enterprises the critical advantage needed to protect their complex networks, secure their devices, and ensure compliance. Over the last 20 years our commitment to innovative technology has earned the trust of more than 1,700 organizations, governments, and institutions globally, solidifying our role in protecting the world’s critical infrastructure and securing our way of life. 

What You Will Be Doing 

• Design, build, and maintain robust data pipelines for real-time and batch processing  

• Develop and optimize data architectures that support data ingestion, transformation, enrichment, and storage, ensuring scalability and efficiency, using AWS technologies 

• Set up and maintain AWS SageMaker environments for model development and deployment 

• Create and manage test datasets with proper versioning techniques using tools  

• Implement monitoring, logging, and alerting for data pipelines to ensure reliability, quality, and accuracy of data outputs 

• Collaborate with Research and ML Engineers to streamline the ML workflow 

• Ensure ML pipelines and model deployments meet security and compliance standards aligned with OPSWAT’s critical infrastructure cybersecurity context 

What We Need From You 

• 3-5 years of experience in data engineering, platform engineering, or MLOps roles 

• Strong proficiency in Python and AWS cloud services 

• Experience with containerization (Docker) and orchestration tools 

• Understanding of ML model deployment and monitoring best practices 

• Experience with AWS SageMaker or similar ML platforms 

• Familiarity with data preparation for machine learning workflows 

Nice to Have 

• Experience with Rust programming language 

• Knowledge of Kafka, Kinesis, or other streaming platforms 

• Experience with additional data orchestration tools and frameworks 

• Advanced capabilities in data visualization tools like PowerBI, Tableau, or similar 

• Knowledge of infrastructure-as-code tools like Terraform or CloudFormation 

• Experience with Feature Stores and ML metadata tracking systems 

• Familiarity with ML experiment tracking tools (MLflow, Weights & Biases) 


• A passion for driving data-driven decisions and fostering a collaborative data culture