The ML Ops Engineer plays a crucial role in bridging the gap between data science and production environments. You will be responsible for streamlining the development, deployment, and management of machine learning models in production.

Responsibilities:

• Collaborate with data science teams to create a seamless pipeline for transitioning ML models from development to production.
• Develop and maintain automated processes for model creation, training, deployment, and updates.
• Implement best practices for model versioning, monitoring, and automated deployment.
• Develop and maintain scalable infrastructure for ML development and deployment initiatives.
• Implement infrastructure as code (IaC) principles to ensure reproducible ML environments.
• Integrate ML pipelines into continuous integration and continuous deployment (CI/CD) workflows.
• Ensure reliable and efficient deployment of ML models into production environments.
• Implement monitoring solutions to track model performance, detect data drift, and ensure data quality.
• Troubleshoot and optimize ML pipelines for maximum efficiency and reliability.
• Implement robust security controls and safeguards for ML systems.
• Ensure compliance with industry standards (e.g., GDPR, HIPAA) during model deployment.
• Collaborate with legal and compliance teams to address regulatory requirements.
• Work closely with data science teams to understand project objectives and translate them into engineering solutions.
• Communicate effectively with stakeholders, including project managers, and cross-functional teams.

What You’ll Bring:

• A relevant Bachelors or higher-education degree in Computer Science, Data Science, or related fields.
• min. 3 years of hands-on experience in cloud engineering, infrastructure, or related roles.
• Minimum of 1 year of professional experience in MLOps, machine learning, and DevOps.
• Preferred certifications in cloud platforms (e.g., AWS, Azure, GCP) and MLOps.
• Proficiency in Python, SQL, and relevant ML libraries (e.g., TensorFlow, PyTorch).
• Expertise in cloud platforms (e.g., GCP, AWS) and containerization (Docker, Kubernetes).
• Strong problem-solving skills and ability to work in a collaborative environment.
• Familiarity with software development methodologies, such as Agile, DevOps, and CI/CD.
• Proficiency in programming languages, including Python and R.
• Experience with software development life cycle.

Offer:

• Employment contract with hybrid model of work
• Work on cutting-edge ML infrastructure projects
• Collaborate with cross-functional teams in a dynamic environment
• Enjoy flexible work arrangements and a culture of innovation