About this role
<div><div style="padding:10.0px 0.0px;border:1.0px solid transparent"><div style="font-size:16.0px;word-wrap:break-word"><H2 style="font-size:1.0em;margin:0.0px">Job Purpose</H2> </div><div></div></div><div style="padding:10.0px 0.0px;border:1.0px solid transparent"><div style="font-size:16.0px;word-wrap:break-word"><H2 style="font-size:1.0em;margin:0.0px">Key Accountabilities</H2> </div><div><ul> <li>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</li> <li>Work collaboratively with data scientists along the machine learning lifecycle from data pipeline, data preparation, model deployment, and model monitoring</li> <li>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</li> <li>Provide architectural and technical leadership to drive AI/ML capabilities</li> <li>Initiate innovation and development projects to continuously improve the overall efficiency of the team in the engineering aspect.</li> </ul></div></div><div style="padding:10.0px 0.0px;border:1.0px solid transparent"><div style="font-size:16.0px;word-wrap:break-word"><H2 style="font-size:1.0em;margin:0.0px">Professional Knowledge & Experiences</H2> </div><div><ul> <li>Degree in computer science or related fields, with concentration in Machine Learning/AI engineering</li> <li>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</li> <li>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.)</li> <li>Knowledge and experience in Kubernetes technology. Be able to develop CI/CD pipeline, deploy workloads, configure and monitor jobs on kubernetes clusters</li> <li>Significant proficiency in Python. Experience working with GCP is preferrable.</li> <li>Ability to work in cross functional teams, have team-work mindset, self-motivation</li> <li>Excellent written and verbal communication skills in English.</li> </ul></div></div><div style="padding:10.0px 0.0px;border:1.0px solid transparent"><div style="font-size:16.0px;word-wrap:break-word"><H2 style="font-size:1.0em;margin:0.0px">Additional Desirable Qualification</H2> </div><div><ul> <li>Solid grounding in statistics, probability theory, data modelling, machine learning algorithms and software development techniques and languages used to implement analytics solutions.</li> <li>Extensive background in statistical analysis and modeling (distributions, hypothesis testing, probability theory, etc.)</li> </ul></div></div><div style="padding:10.0px 0.0px;border:1.0px solid transparent"><div style="font-size:16.0px;word-wrap:break-word"><H2 style="font-size:1.0em;margin:0.0px">CORE Competencies</H2> </div><div></div></div></div>