onsite
Senior Machine Learning Operations Engineer - Mercury
ML Engineer
Lead end‑to‑end MLOps for real‑time fraud and financial crime models, building scalable pipelines on AWS, containerizing with Docker, orchestrating on Kubernetes, and ensuring robust observability and rapid deployment cycles.
About the role
Key Responsibilities
- Design, implement, and maintain production ML pipelines from model registry to real‑time inference services.
- Containerize models with Docker and deploy them on Kubernetes clusters, ensuring high availability and scalability.
- Integrate CI/CD workflows for automated testing, validation, and deployment of ML artifacts.
- Implement comprehensive observability: logging, metrics, tracing, and alerting for model performance and drift.
- Collaborate with data scientists and software engineers to optimize model serving latency and throughput.
Requirements
- 5+ years of experience in MLOps or production ML engineering.
- Proficiency with Python, AWS services (ECS/EKS, S3, SageMaker), Docker, and Kubernetes.
- Strong background in CI/CD pipelines (GitHub Actions, Jenkins, ArgoCD) and infrastructure as code.
- Experience building observability solutions using Prometheus, Grafana, or similar tools.
- Excellent problem‑solving skills and a passion for building reliable, scalable systems.
Skills
pythonmachine learningawsdockerkubernetescicd