ML Ops Engineer

KEY ACCOUNTABILITIES

• Implement and support end-to-end MLOps pipelines using GCP, Vertex AI, Airflow/Kubeflow, and related tools.
• Build and maintain feature engineering, deployment, monitoring, retraining, and CI/CD/CT pipelines for production ML solutions.
• Ensure ML model lifecycle management, including monitoring, performance optimization, and operational support.
• Follow and recommend MLOps best practices, coding standards, version control, and technical documentation.
• Collaborate with Data Science, Data Engineering, Cloud Platform, and MLOps teams to deliver scalable ML solutions.
• Communicate project progress, risks, and outcomes effectively while contributing to knowledge sharing across teams.
• Continuously enhance technical expertise by adopting emerging MLOps technologies, tools, and best practices.

MINIMUM QUALIFICATIONS

• **Education:**Bachelors degree (full time)
• Experience: Total 6+ years of analytical experience with atleast 3+ years of experience in AI and Machine Learning
• Technical Skills: Expertise in Data Transformation and Manipulation through Big-Query/SQL, professional experience with Vertex AI and GCP Services, Strong expertise in Python for designing and running ML pipelines along with Airflow/Cloud composer/Kubeflow Experience, Building and maintaining project specific custom containers.
• Soft Skills: Strong communication skills both verbal and written including the ability to interact effectively with colleagues of varying technical and non-technical Passionate about agile software processes, data-driven development, reliability, and systematic experimentation.

PREFERRED QUALIFICATIONS

• Google Cloud Platform Machine Learning (GCPML) certification
• Understanding of the Consumer-Packaged Goods (CPG) industry
• Strong understanding of Core Machine Learning Algorithms

ELIGIBILITY

Applicants must meet minimum age qualifications in the country in which the job is located.