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We are seeking a Senior MLOps Engineer to join a major company that aggregates broad and deep property data and generates analytics informing smart decision-making and property-level insights relied upon by hundreds of thousands of users in the real estate industry.
In this role, you will lead the transformation of systems to the cloud, driving cost optimization, improved availability and reliability through architectural improvements, and enhanced analytics with better TTM. You will own and evolve the end-to-end process of taking machine learning models to production, as well as establishing best practices for maintaining, monitoring, and continuously improving them. You will also mentor junior engineers, define technical standards, and collaborate with cross-functional stakeholders to shape the ML platform strategy.
- Define and govern the ML systems lifecycle across multiple teams and projects
- Translate complex business objectives into well-framed ML problems and technical roadmaps
- Architect scalable, secure, and cost-efficient data solutions tailored to diverse use cases
- Oversee data preparation and processing strategies, ensuring quality, lineage, and reproducibility
- Lead model training and experimentation initiatives, establishing frameworks and standards for the team
- Design, build, and automate robust, reusable ML pipelines and CI/CD workflows
- Deploy models to production systems with a focus on scalability, security, and performance
- Monitor, optimize, and maintain models in production, implementing advanced observability, drift detection, and automated remediation
- Mentor and provide technical leadership to junior and middle engineers
- Collaborate with Data Science, Engineering, and Product teams to align ML initiatives with business goals
- Drive architectural decisions and contribute to the long-term MLOps strategy
- 3+ years of experience in an MLOps or similar role
- Advanced proficiency in Python, with strong software engineering practices
- Deep expertise in Google Vertex AI and Google Vertex AI Pipelines, including production-scale implementations
- Proven experience designing and implementing monitoring, alerting, and observability for ML models in production
- Strong hands-on experience with Jenkins and Docker in enterprise CI/CD environments
- Experience architecting end-to-end ML platforms and establishing MLOps best practices
- English proficiency at B2 level or above
- Background in AI Solution Engineering and Prompt Engineering
- Knowledge of Agentic SDKs and Google Agent Development Kit
- Experience with Google Cloud Platform, Ansible, and Terraform
- Familiarity with Kubeflow and Kubernetes
- Proficiency in Groovy and JetBrains PyCharm