Sr. ML Engineer
Core
Design, build, and manage scalable cloud infrastructure for AI and Machine Learning applications, focusing on MLOps, model deployment, and platform security.
Role type
Senior IC MLOps and ML Infrastructure Engineer
Builds
Secure, scalable ML serving infrastructure and pipelines for traditional ML and Generative AI (LLM) workloads
Domain
Payments technology, Cloud Infrastructure, Machine Learning Operations
Deliverable
infrastructure
Required skills
AWS cloud architecture, Kubernetes cluster management, Kubeflow pipeline orchestration, MLOps practices, CI/CD pipeline development, Infrastructure as Code (Terraform/CloudFormation), Cloud security (IAM, VPCs), Model serving frameworks (vLLM, TensorRT-LLM, KServe, Triton), Observability tools (CloudWatch, Prometheus, Grafana)
Preferred skills
Advanced degree (Masters, MBA, JD, MD), Cloud-agnostic experience, Legacy pipeline modernization
Technologies
AWS, Kubernetes, Kubeflow, vLLM, TensorRT-LLM, KServe, Triton, Terraform, AWS CloudFormation, GitHub Copilot, ChatGPT, Claude Code, CLine, CloudWatch, Prometheus, Grafana
Responsibilities
Design and maintain scalable ML infrastructure on AWS and OnPrem; Deploy and manage Kubernetes clusters and Kubeflow; Build robust serving infrastructure for ML models and LLMs; Design secure platform architectures using AWS IAM and VPCs; Architect scalable cloud systems and automate provisioning; Develop automated CI/CD pipelines for model training and deployment; Partner with Data Scientists and AI Engineers to reduce development friction; Implement logging, monitoring, and alerting for system health and model drift; Act as a technical guide to modernize legacy deployment pipelines
Seniority
Senior, hands-on IC
