Machine Learning Engineer
Core
Build and scale MLOps infrastructure to take machine learning models from notebook to production, integrating streaming data and agentic capabilities.
Role type
Mid-level Machine Learning Engineer (MLOps focus)
Builds
MLOps platform, production serving infrastructure, streaming data pipelines, agentic workflows
Domain
Fintech / Payments / AI Infrastructure
Deliverable
production ML models
Required skills
MLOps framework design, model serving, streaming systems integration, containerization, observability, CI/CD for ML, automation of retraining pipelines
Preferred skills
LLM and agent frameworks, regulated domain experience, open source contributions, cloud ML platforms
Technologies
Kafka, Kinesis, Flink, Seldon, KServe, BentoML, TorchServe, Airflow, Kubeflow, MLflow, Docker, Kubernetes
Responsibilities
Design and maintain MLOps platform with experiment tracking and model registry; build low latency high availability serving infrastructure; automate retraining and evaluation pipelines; integrate ML models with streaming data platforms; design agentic workflows and observability frameworks
Seniority
Mid-level, hands-on IC
