onsite
ML Platform Engineer - Truist Bank
Devops Engineer
Lead the design, deployment, and scaling of machine learning pipelines on cloud infrastructure, leveraging Python, AWS, Kubernetes, and CI/CD to deliver robust, production‑grade ML services.
About the role
Key Responsibilities
- Architect and maintain end‑to‑end ML pipelines, from data ingestion to model serving, using Python and AWS services.
- Containerize models and services with Docker, orchestrate with Kubernetes, and automate deployments via CI/CD pipelines.
- Collaborate with data scientists to translate research prototypes into scalable production systems.
- Implement monitoring, logging, and alerting to ensure high availability and performance of ML workloads.
- Optimize resource utilization and cost across cloud environments, applying best practices for security and compliance.
Requirements
- 3+ years of experience building ML platforms or data engineering solutions.
- Proficiency in Python, AWS (SageMaker, ECS, EKS, S3), and container orchestration.
- Hands‑on experience with CI/CD tools (GitHub Actions, Jenkins, ArgoCD) and infrastructure as code.
- Strong understanding of model versioning, reproducibility, and MLOps principles.
- Excellent problem‑solving skills and ability to work cross‑functionally in a fast‑paced environment.
Skills
pythonmachine learningawskubernetesdockercicd