Full-stack Engineer

• ML Platform & Infrastructure Design and implement scalable infrastructure for training, fine-tuning, and serving open source LLMs (Llama, Mistral, Gemma, etc.)
• Architect and manage Kubernetes clusters for ML workloads, including GPU scheduling, autoscaling, and resource optimization
• Design, implement, and maintain feature stores for ML model training and inference pipelines
• Build and optimize LLM inference systems using frameworks like vLLM, TensorRT-LLM, and custom serving solutions
• Ensure 99.9%+ uptime for ML platforms through robust monitoring, alerting, and incident response procedures
• Design and implement ML platforms using DataRobot, Azure Machine Learning, Azure Kubernetes Service (AKS), and Azure Container Instances
• Develop and maintain infrastructure using Terraform, ARM templates, and Azure DevOps
• Implement cost-effective solutions for GPU compute, storage, and networking across Azure regions
• Ensure ML platforms meet enterprise security standards and regulatory compliance requirements
• Evaluate and potentially implement hybrid cloud solutions with AWS/GCP as backup or specialized use cases
• Design and maintain robust CI/CD pipelines for ML model deployment using Azure DevOps, GitHub Actions, and MLOps tools
• Implement automated model training, validation, deployment, and monitoring workflows
• Set up comprehensive observability using Prometheus, Grafana, Azure Monitor, and custom dashboards
• Continuously optimize platform performance, reducing latency and improving throughput for ML workloads
• Design and implement backup, recovery, and business continuity plans for ML platforms
• Mentor junior engineers and data scientists on platform best practices, infrastructure design, and ML operations
• Lead comprehensive code reviews focusing on scalability, reliability, security, and maintainability
• Design and deliver technical onboarding programs for new team members joining the ML platform team
• Establish and champion engineering standards for ML infrastructure, deployment practices, and operational procedures
• Collaborate with product engineering teams to integrate ML capabilities into customer-facing applications

Senior Staff Software Engineer – AI/ML Platform

Lead Software Engineer, Data Platform

Senior Software Engineer, Data Platform

Principal Software Engineer, Data Platform

Software Engineer II, Care Delivery – Full Stack

Manager, Full Stack Engineer