Machine Learning Ops Engineer
Core
Build production-grade AI systems and end-to-end MLOps/LLMOps pipelines to power financial products.
Role type
Machine Learning Ops Engineer
Builds
Production-ready AI services, scalable ML pipelines, and automated deployment workflows for financial products.
Domain
Financial services / Machine Learning Operations
Deliverable
production ML models
Required skills
MLOps pipeline design, CI/CD implementation, containerization, Kubernetes orchestration, AWS services, monitoring and alerting, model versioning, API development
Preferred skills
LLMOps experience, Terraform, Prometheus, Grafana, Mlflow
Technologies
Bitbucket, Jenkins, Docker, Kubernetes, EKS, AWS SageMaker, ECR, Lambda, Step Functions, S3, CloudWatch, CloudFormation, Terraform, Prometheus, Grafana, Mlflow
Responsibilities
Design and maintain end-to-end MLOps and LLMOps pipelines; Implement CI/CD workflows for automated testing and release; Containerize ML workloads and orchestrate on Kubernetes; Leverage AWS services to host and manage model training and inference; Develop monitoring solutions for model latency, accuracy, and data drift; Automate model versioning and metadata tracking; Collaborate with data scientists to package models as production-ready services; Ensure security, compliance, and governance across the ML lifecycle.
