ML Ops Engineer

Role Summary

• The ML Ops Engineer will be responsible for designing building and maintaining the infrastructure and processes for deploying and managing machine learning models in production
• Responsibilities
• Understand and translate business and functional needs into machine learning problem statements
• Translate complex machine learning problem statements into specific deliverables and requirements
• Design and develop scalable solutions that leverage machine learning and deep learning models to meet enterprise requirements
• Translate machine learning algorithms into productionlevel code
• Collaborate with development teams to test and deploy machine learning models
• Monitor the performance of deployed models track data or concept drift and update or retrain models as needed
• Ensure adherence to performance standards and compliance with data security requirements
• Keep abreast with new tools algorithms and techniques in machine learning and work to implement them in the organization
• Proficient in Python scripting and familiarity with Python packaging (e.g. PyPI, pip, virtualenv)
• Hands on experience with ML workflow tools like MLflow, Kubeflow, MLRun, DVC, Airflow leveraging Python integrations.
• Automate model training, testing and deployment processes using CI/CD tools.
• Experience with cloud platform preferably GCP and their Python SDKs.
• Experience with SQL and Python-based ETL processes.
• Familiarity with data processing frameworks like Apache, Spark or Dask using PySpark or similar Python interfaces.
• Collaborate with development teams to test, optimize ML workflows and deploy/integrate machine learning models into applications.
• Design and develop scalable solutions that leverage machine learning and deep learning models to meet enterprise requirements
• Translate machine learning algorithms into production-level code
• Knowledge of version control systems (e.g. git) and collaborative coding practices.
• Monitor the performance of deployed models, track data or concept drift, and update or retrain models as needed
• Ensure adherence to performance standards and compliance with data security requirements
• Keep abreast with new tools, algorithms and techniques in machine learning and work to implement them in the organization

Education

• A bachelors degree in computer science data science applied mathematics software engineering or related masters degree preferred
• Specialization in applied machine learning or machine learning infrastructure preferred
• Experience
• 5-7 years of experience in developing and deploying enterprise scale machine learning solutions in a software engineering adjacent field
• Experience developing and debugging in Python
• Exposure to architectural patterns of largescale software applications
• Experience with REST API development in Python (e.g. Flask, Fast API) for model serving.
• Experience in developing, trouble shooting and resolving issues related to ML systems in production.
• Understanding of DevOps principles and exposure to architectural patterns of large-scale software applications

Required skills

• Knowledge of working on any Cloud Environment GCP preferred
• Proficiency in deploying machine learning algorithms as production ready API services
• Advanced programming skills with Python
• Ability to effectively communicate technical concepts and results to technical and business audiences in a comprehensive manner
• Ability to collaborate effectively across multiple teams and stakeholders including analytics teams development teams product management and operations
• Ability to work independently and in a fully remote environment
• Willingness and ability to stay up to date on new MLAI technologies and their potential impact on the company