About this role
Your Responsibilities:
Take ownership of the technical direction and execution of machine learning projects within the Data Science team; Mentor and support junior engineers and data scientists, enabling their growth and success;Establish best practices for software design, architecture, and MLOps;Design and optimize end-to-end machine learning pipelines for training, testing, and deployment in production environments;Ensure seamless deployment of machine learning models and implement monitoring solutions to track performance, accuracy, and drift over time;Collaborate with data scientists to transition experimental models to production-grade solutions;Work closely with other software/data engineers and product teams to ensure effective delivery of machine learning products;Collaborate with other teams and gather requirements to solve complex customer problems with machine learning;Maintain and scale machine learning infrastructure using GCP;Implement robust security measures to protect data privacy;Create and maintain APIs or other interfaces to deliver machine learning results to internal teams and Metro’s customers;Evaluate emerging machine learning technologies, frameworks, and trends, and introduce improvements to existing workflows. Required key competencies and qualifications:
8 years of hands-on experience in building and deploying machine learning models in production. Strong technical background and current modern BI and reporting technologies; Proven experience in leading or mentoring technical teams;Proficiency in Python, Go and machine learning frameworks; Strong understanding of MLOps tools and practices, including CI/CD pipelines, model versioning, and monitoring;Proficient with data transformation tools (preferable dbt);Expertise in GCP services such a Cloud Run, Kubernetes, BigQuery and VertexAI;Solid skills in API development and automated testing;Exceptional problem-solving abilities and a proactive, team-oriented mindset;Strong communication and collaboration skills, with the ability to explain technical concepts to non-technical stakeholders;Leadership qualities with a track record of driving projects and guiding teams toward success;Experience in optimizing models for performance (e.g., inference speed, resource usage);Understanding of ethical AI, bias mitigation, and data privacy principles.