Key skills
AirflowCloudDistributed SystemsKubernetesMLJupyterMLflowPerformance Optimization
About this role
Role Overview
- Design and implement enterprise-scale ML infrastructure
- Architect high-performance model serving solutions handling millions of predictions per second
- Build robust, scalable systems for model training, serving and inferencing
- Lead technical decisions for critical ML platform components
- Develop automated ML pipelines using “State of art” tools
- Implement sophisticated monitoring and observability solutions
- Optimize resource utilization across large scale distributed computing environments
- Design fault-tolerant, highly available ML systems using Airflow or MLflow, HPC (SLURM)/ GPU
Requirements
- 10+ years of software engineering experience with distributed systems
- 4+ years of hands-on experience building ML platforms
- Proven experience with Kubernetes, containerization, and cloud platforms
- Strong background in performance optimization and scalability
- Experience with Jupyter Hub, MLflow, or similar ML platforms
Tech Stack
- Airflow
- Cloud
- Distributed Systems
- Kubernetes
Benefits
- Market leader in compensation and equity awards
- Comprehensive physical and mental wellness programs
- Competitive vacation and holidays for recharge
- Paid parental and adoption leaves
- Professional development opportunities for all employees regardless of level or role
- Employee Networks, geographic neighborhood groups, and volunteer opportunities to build connections
- Vibrant office culture with world class amenities
- Great Place to Work Certified™ across the globe