Senior AI Deployment Engineer

Stryker

Amsterdam, Netherlands

As a Senior AI deployment engineer, you will work along with a team of AI scientists, AI and xR application engineers, software engineers and clinical experts to design, develop, and deploy computer vision, augmented reality, mixed reality, multi-modal AI models and GenAI features into existing and new medical device products. This is a unique, high visibility opportunity for talented individual who wants to dive deep into cutting-edge AI and GenAI optimization for cloud and edge deployments.

What you will do:

Develop and deploy Artificial Intelligence (AI) powered software on cloud and edge devices (iPhone, iPad, Vision Pro, Android devices, NVidia devices).
Optimize AI/ML models and pipelines for real-time inference on edge devices.
Build containerized AI services (e.g. Docker) and orchestrate deployments.
Deploy and maintain real-time microservices for AI applications including GenAI apps.
Work with MLOps and AI security platform team to support continuous integration, testing, and monitoring of AI models
Design deployment evaluation frameworks, develop unit tests for software components in compliance with regulatory requirements
Generate and review the necessary documents with project teams.
Perform Software verification and/or validation testing.
Perform code reviews as an independent reviewer following best coding standards and practices.

Who you are:

Bachelor's degree in software engineering/Computer Science or related discipline with 2+ years of relevant work experience OR Master’s in relevant disciplines OR PhD degree in relevant disciplines.
At least 4+ years of Python and C++ development experience.
3+ years of experience developing and deploying AI/ML models into production environments.
Proficiency in containerization tools (Docker, Docker Compose).
Experience with CI/CD Git automation pipelines.
Strong Cloud deployment experience (Azure, GCP or AWS) .
Proven ability to optimize AI models for real-time inference on edge devices to meet latency and performance requirements.
Knowledge of model optimization techniques (ONNX Runtime, quantization, pruning, TensorRT, OpenVINO, CoreML etc.).
Experience with voice, LLMs and Generative AI (LLMs, vision, multimodal, multiagent) microservices experience.
Experience with multi-agentic AI frameworks for orchestrating complex workflows.
Experience with Kubernetes for orchestrating AI microservices.
Familiarity with monitoring and logging (Prometheus, Grafana, Azure Monitor, etc.).
Experience with medical devices and product development standards in a regulated environment (ISO 13485, IEC 62304, ISO 14971).