AI Platform Engineer
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Job Description
AI Platform Engineer operating and automating production AI services for Deluxe. Improving model deployment, observability, reliability, and delivery across global media workflows.
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
- Develop automation for model packaging, promotion, validation, rollout, rollback, and lifecycle management
- Implement monitoring, logging, alerting, and observability for AI services, including performance, latency, availability, error rates, and cost
- Partner with AI Engineers to move models, workflows, and AI services from prototype to production
- Partner with cloud platform, infrastructure, and application teams to ensure AI services run on secure, scalable, and reliable environments
- Help define operational standards for production AI systems, including runbooks, incident response, testing, and release readiness
- Support batch and real-time AI workloads where applicable
- Troubleshoot issues related to model serving, API behavior, environment configuration, infrastructure, and deployment pipelines
- Build reusable patterns for AI service deployment and integration across Deluxe applications
- Contribute to evaluation and adoption of 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 concepts, model 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 used 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
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