About the Role
This position leads the design and implementation of scalable data platforms and machine learning systems, supporting enterprise-wide initiatives through technical innovation and cross-functional collaboration.
Responsibilities
- Lead architecture and development of data-intensive systems
- Design and deploy machine learning models into production environments
- Collaborate with product and engineering teams to define technical roadmaps
- Ensure data pipelines are reliable, scalable, and secure
- Mentor engineers in best practices for data engineering and AI
- Evaluate emerging technologies for data and AI integration
- Drive automation of data workflows and model deployment
- Support data governance and compliance standards
- Optimize query performance across large datasets
- Integrate AI capabilities into existing software platforms
- Conduct code reviews and system design evaluations
- Troubleshoot complex issues in distributed data systems
- Develop APIs for data access and model serving
- Implement monitoring and alerting for data pipelines
- Promote reusability and standardization of data components
- Work with stakeholders to translate business needs into technical solutions
- Ensure system designs align with long-term scalability goals
- Contribute to technical documentation and knowledge sharing
- Participate in incident response for critical data services
- Support testing and validation of AI model outputs
- Enforce security practices in data handling and storage
- Collaborate on data quality assurance processes
- Guide selection of data storage technologies
- Lead proof-of-concept initiatives for AI applications
- Foster a culture of innovation and technical excellence
Compensation
Competitive salary with performance-based incentives
Work Arrangement
Hybrid work model with flexible scheduling
Team
Collaborative engineering unit focused on data systems and artificial intelligence
About the Team
This group specializes in building foundational data systems and integrating artificial intelligence across business functions. The team operates with technical autonomy while aligning with enterprise objectives, emphasizing robust design and operational excellence.
Technology Stack
Primary tools include Apache Spark, Kafka, TensorFlow, Airflow, Kubernetes, and cloud-native data services. The environment emphasizes automation, observability, and scalable architecture patterns.
Available for qualified candidates
