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.
