Lead production support, troubleshoot issues, perform root cause analysis, and implement preventive solutions.
Partner with Data Science, Engineering, and Business teams to deploy scalable, production-ready ML solutions.
Drive ML architecture standards, reusable design patterns, and platform improvements across the organization.
Research and adopt emerging MLOps technologies and best practices to enhance scalability and reduce cloud costs.
Mentor team members, promote knowledge sharing, and foster a collaborative engineering culture.
Continuously enhance technical expertise through learning and adoption of new technologies.
MINIMUM QUALIFICATIONS
Education: Minimum Bachelor's degree, Advanced degree in a quantitative field (CS, engineering, statistics, math, data science).
Experience: Relevant Machine Learning experience of 7+ years in MLOps and overall 12+ years of Industry experience
Technical Skills
Strong proficiency in Python, SQL/BigQuery, and Vertex AI on GCP.
Experience building and deploying production-scale ML models with performance optimization.
Hands-on experience with Airflow, Kubeflow, MLflow, and MLOps orchestration.
Knowledge of CI/CD, TDD, Jenkins, and version control tools such as Git.
Experience working in Agile (Scrum/Kanban) development environments.
Strong understanding of supervised ML algorithms, data transformation, and feature engineering.
Passion for learning new technologies and solving complex engineering problems.
Soft Skills: Strong verbal and written communication skills including the ability to interact effectively with colleagues of varying technical and non-technical abilities.
PREFERRED QUALIFICATIONS
GCP Machine Learning certification, Understanding of CPG industry
Exposure to Deep Learning/RL/LLMs
Publications or contributions to the data science and AI community.
Certifications in AI, machine learning, or related fields.
Applicants must meet minimum age qualifications in the country in which the job is located.
