Lead D&T Machine Learning Engineer

KEY ACCOUNTABILITIES

Establish and Implement MLOps practices:

• Development of end-to-end MLOps framework and Machine Learning Pipeline using GCP, Vertex AI, and Software tools
• Serving Pipeline with multiple creation Vertex AI and GCP services. Improve ML pipeline documentation and understandability.
• Automate logging of model usage and predictions provided. Improve logging and diagnostic processes.
• Automate monitoring of models both for failures and degradation. Automate monitoring of data sources to identify issues and/or data changes.
• Design and implement dynamic re-training of ML pipelines using event-based or custom logic.
• Resource and Infra Monitoring configuration and pipeline development using GCP service.
• Branching strategies and Version Control using GitHub
• ML Pipeline orchestration and configuration using Airflow/Kubeflow.
• Code refactorization & coding best practices implementation as per industry standard

Implementing MLOps practices on a project and establishing MLOps best practices.

• Lead the investigation and resolution of production issues, perform root cause analysis, and recommend changes to reduce/eliminate re-occurrence of issues.
• Optimize deployment and change control processes for models.
• Create and operationalize quality assurance processes for ML models.

Lead the execution of ML Solutions @Scale:

• Partners with business stakeholders to design the right deliver value-added insights and intelligent solutions through ML and AI.
• Collaborates with Data Science Leads, ML System Engineering and Platform teams to ensure the models are deployed in a scaled and optimized way. Additionally, ensure support the post-production to ensure model performance degrades are proactively managed.
• Play a lead role in spearheading the development effort of new standards (design patterns, coding practices, orchestration patterns) and drive value and adoption across the Data Science team.
• Is considered an expert in the ML Ops and Model management space; brings together business knowledge, architecture, resources, people, and technology to create more effective solutions.

Research, Evolve and Publish best practices:

• Research and operationalize technology and processes necessary to scale ML Ops
• Recommend model changes to optimize cloud spend.
• Ability to research and recommend MLOps best practices on new technologies, platforms, and services.
• Drive ideation, design, and creation of new ML Architecture patterns in discussion with the Enterprise Architecture team.
• MLOps pipeline improvement plan and suggestion

Communication and Collaboration:

• Knowledge sharing with the broader analytics team and stakeholders.
• Communicate on the on-goings to embrace the remote and geographical culture.
• Ability to communicate the accomplishments, failures, and risks in timely manner.
• Knowledge sharing session with team for specific ML Ops topics. Coach and Mentor junior ML members in the team.
• Foster a collaborative and innovative team environment. Contribute to the overall effort to educate stakeholders on AI practices.
• Closely collaborates with the stakeholders on projects and data science leaders to ensure practices are developed and enhanced to support accelerated analytic development and maintainability.

Embrace a learning mindset:

• Continually invest in one’s knowledge and skillset through formal training, reading, and attending conferences and meetups

MINIMUM QUALIFICATIONS

• Full time graduate from an accredited University.
• Advanced degree in a quantitative field (CS, engineering, statistics, math, data science).
• Proven technical leadership in a large, complex matrixed organization.
• Relevant Machine Learning experience of 6+ years and overall 12+ years of Industry experience.
• Experience in supervised ML algorithms, optimization, and performance tuning.
• Track record of producing machine learning models and production infrastructure at scale.
• Strong verbal and written communication skills including the ability to interact effectively with colleagues of varying technical and non-technical abilities.
• Passionate about agile software processes, data-driven development, reliability, and systematic experimentation.
• Passion for learning new technologies and solving challenging problems.
• Good understanding of CI, CD, TDD, and tools such as Jenkins.
• Strong understanding of orchestration frameworks such Airflow/Kubeflow/MLFlow.
• Agile software development experience such as Kanban and Scrum.
• Experience in software version control team practices and tools such as GIT and TFS.
• Expertise in Data Transformation and Manipulation through Big-Query/SQL
• Professional experience with Vertex AI and GCP Services.
• Strong proficiency in Python.

PREFERRED QUALIFICATIONS

• GCP Machine Learning certification
• Understanding of CPG industry
• Exposure to Deep Learning/RL/LLMs
• Prior experience with CPG industry.
• Publications or contributions to the data science and AI community.
• Certifications in AI, machine learning, or related fields.