Senior Engineer- Machine Learning Platform

About the Role:

Role & responsibilities

Architecture & Development

• Design and implement enterprise-scale ML infrastructure using Ray, Kubernetes, and cloud-native technologies
• Architect high-performance model serving solutions handling millions of predictions per second
• Build robust, scalable systems for model training, deployment, and monitoring
• Lead technical decisions for critical ML platform components

MLOps & Infrastructure

• Develop automated ML pipelines using Airflow and MLflow
• Implement sophisticated monitoring and observability solutions
• Optimize resource utilization across distributed computing environments
• Design fault-tolerant, highly available ML systems

Performance Engineering

• Optimize large-scale distributed systems for maximum throughput
• Implement advanced memory management strategies for ML workloads
• Design and optimize real-time inference systems
• Tune system performance for production-grade ML operations

Preferred candidate profile

• 8+ years of software engineering experience with distributed systems
• 4+ years of hands-on experience building ML platforms
• Deep expertise in Python and modern ML infrastructure tools
• Proven experience with Kubernetes, containerization, and cloud platforms
• Strong background in performance optimization and scalability
• Experience with Ray, JupyterHub, MLflow, or similar ML platforms
• Distributed Systems: Ray, Kubernetes, Docker or similar large scale distributed systems
• ML Platforms: MLflow, Kubeflow, JupyterHub, Kubeflow
• Infrastructure: AWS/GCP/Azure, Terraform
• Languages: Python, Go
• Observability: Prometheus, Grafana
• CI/CD: GitLab, Jenkins

What Sets You Apart:

• Contributions to open-source ML infrastructure projects
• Experience with real-time, high-throughput inference systems
• Background in cybersecurity or threat detection
• Track record of leading technical initiatives
• Experience with large-scale data processing systems