Data Engineer- Customer Recommender

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.