Machine Learning Engineer

Key Responsibilities

• Design and work on all aspects of bringing ML models into production, develop CI/CD pipelines by collaborating with other disciplines such as data engineering, application development, cloud infrastructure, and security to implement AI solutions in production
• Work collaboratively with data scientists along the machine learning lifecycle from data pipeline, data preparation, model deployment, and model monitoring
• Understand and assess AI/ML industry trends to leverage technologies, continuously improve efficiency and effectiveness of the existing algorithms; as well as to understand their impact on our AI/ML solutions
• Provide architectural and technical leadership to drive AI/ML capabilities
• Initiate innovation and development projects to continuously improve the overall efficiency of the team in the engineering aspect.

Professional Knowledge and Experience

• Degree in computer science or related fields, with concentration in Machine Learning/AI engineering
• At least 3 years of experience in implementing and deploying Machine Learning solutions (using various models, such as Linear/Logistic Regression, Support Vector Machines, Neural Networks, etc.). Expertise with Data Science and experience with manipulating/transforming data, model selection, model training, and deployment at scale
• Knowledge and experience in database technologies, such as SQL, NoSQL, and demonstrate knowledge of databases (Google BigQuery preferred), Data ETL framework (Airflow), ML libraries (scikit-learn, XG Boost, PyTorch, etc.), ML Frameworks (Kubeflow, MLFlow, etc.)
• Knowledge and experience in Kubernetes technology. Be able to develop CI/CD pipeline, deploy workloads, configure and monitor jobs on kubernetes clusters
• Significant proficiency in Python. Experience working with GCP is preferrable.
• Ability to work in cross functional teams, have team-work mindset, self-motivation
• Excellent written and verbal communication skills in English.

Additional Desirable Qualification

• Solid grounding in statistics, probability theory, data modelling, machine learning algorithms and software development techniques and languages used to implement analytics solutions.
• Extensive background in statistical analysis and modeling (distributions, hypothesis testing, probability theory, etc.)