onsite
AI Operations Engineer - BlytzPay
Systems Engineer
AI Operations Engineer responsible for bridging AI platform architecture to production, driving agent skill development, and ensuring accurate AI execution using Python, Machine Learning, AWS, Docker, Kubernetes, and CI/CD pipelines.
About the role
Key Responsibilities
- Design and implement production‑ready AI pipelines that translate research prototypes into scalable services.
- Develop and maintain agent skill modules, ensuring they integrate seamlessly with core payment and automation workflows.
- Monitor AI model performance, troubleshoot drift, and collaborate with data scientists to refine training data.
- Automate deployment using containerization (Docker) and orchestration (Kubernetes) on AWS infrastructure.
- Build CI/CD pipelines to support rapid iteration and continuous delivery of AI features.
Requirements
- Strong programming experience in Python and familiarity with ML libraries (scikit‑learn, PyTorch, TensorFlow).
- Hands‑on experience deploying models to AWS services (SageMaker, ECS, EKS) and managing Docker/Kubernetes clusters.
- Solid understanding of CI/CD concepts and tools (GitHub Actions, Jenkins, ArgoCD).
- Ability to translate business requirements into technical specifications and troubleshoot end‑to‑end workflows.
- Excellent communication skills and a collaborative mindset.
Skills
pythonmachine learningawsdockerkubernetescicd