AI Platform Engineer
Deluxe Corporation9 days ago
Remote, GermanyMid Level
Responsibilities
- Build and operate platforms for deploying, monitoring, and maintaining production AI models and services.
- Support model-serving environments, inference pipelines, deployment workflows, and release automation.
- Automate model packaging, promotion, validation, rollout, rollback, and lifecycle management.
- Implement monitoring, logging, alerting, and observability for AI service performance, latency, availability, errors, and cost.
- Partner with AI Engineers to move models, workflows, and AI services from prototype to production.
- Collaborate with cloud platform, infrastructure, application, and product teams to deliver secure, scalable, and reliable AI services.
- Define operational standards including runbooks, incident response, testing, and release readiness.
- Troubleshoot model-serving, API, environment, infrastructure, and deployment-pipeline issues.
- Build reusable patterns for AI service deployment and integration across Deluxe applications.
- Evaluate and adopt MLOps tools, model-serving frameworks, observability platforms, and AI infrastructure technologies.
Requirements
- Experience in MLOps, AI platform engineering, DevOps, software engineering, or production ML operations.
- Experience deploying or operating AI/ML models, inference services, data services, or API-based production systems.
- Strong scripting or programming skills using Python or similar languages.
- Experience with containers, CI/CD, cloud environments, monitoring, and operational automation.
- Understanding of model deployment, lifecycle management, versioning, validation, and rollback.
- Experience troubleshooting production systems across application, model, infrastructure, and deployment layers.
- Preferred experience with model-serving frameworks, MLOps platforms, Kubernetes, Docker, ECS, EKS, MLflow, KServe, BentoML, Ray, Airflow, or similar technologies.
- Preferred experience supporting LLMs, agentic workflows, RAG systems, speech models, translation models, or other applied AI services.
- Preferred experience with GPU-backed inference, batch processing, distributed systems, or high-throughput workloads.
- Preferred experience with AWS services for compute, storage, networking, security, monitoring, and deployment.
- Preferred experience with media, localization, dubbing, content workflows, ASR, MT, TTS, or language technologies.
- Preferred experience evaluating or integrating commercial and open-source AI platforms.
Tech Stack
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