Job Opening: Freelance AI Engineer – Healthcare AI Platform
Location: Remote
Contract Duration: 12 months
Start Date: ASAP
Language: Dutch preferred (clinical/domain-specific content)
Role
We are seeking a Freelance AI Engineer to help transform experimental AI models into reliable, production-grade systems used in real clinical environments. This role focuses on building and maintaining AI infrastructure and applications that support clinicians through advanced speech, language, and agentic AI systems.
You will work across the full lifecycle of AI systems – from model experimentation and evaluation to deployment, monitoring, and iterative improvement – ensuring solutions are robust, scalable and safe for healthcare use.
Impact
In this role, you will:
- Enable clinicians to work more efficiently by integrating practical AI tools directly into their workflows
- Develop speech-to-text and audio processing systems to support clinical documentation and structured data capture
- Build and maintain agentic AI workflows and evaluation harnesses to ensure reliable model behaviour in production
- Deliver production-ready AI capabilities that healthcare professionals can trust
- Promote transparent, responsible AI practices across the product ecosystem
- Contribute to the long-term architecture and technical direction of the organisation’s AI platform
Key Responsibilities
- Build and deploy large language model (LLM) and speech-to-text systems for healthcare applications such as clinical transcription, summarisation, and structured note generation
- Develop and maintain audio and speech data pipelines, including ingestion, preprocessing, and model evaluation
- Design agentic AI systems, including orchestration layers, evaluation frameworks, and reliability harnesses for autonomous or semi-autonomous workflows
- Take ownership of AI functionality from model experimentation and evaluation through production deployment and monitoring
- Build scalable ML infrastructure, including model evaluation pipelines, prompt orchestration, and automated testing frameworks
- Develop real-time and batch inference pipelines using Python/Go, containerisation, and Linux-based environments
- Implement CI/CD pipelines for machine learning systems, ensuring reproducibility and reliability in deployment
- Monitor and analyse model performance in production, iterating based on user feedback and usage data
- Collaborate with product teams, engineers, and clinical stakeholders to translate real-world feedback into model and system improvements
- Design human-in-the-loop systems that keep clinicians in control of AI-generated outputs
Requirements
- 3+ years of experience building and deploying machine learning or AI systems in production environments
- Strong programming skills in Python or Golang
- Solid experience working in Linux-based environments and building production-grade systems
- Experience with speech/audio data pipelines, speech-to-text models, or ASR systems
- Hands-on experience with large language models, including fine-tuning, evaluation, and deployment
- Experience building agentic AI workflows, orchestration systems, or evaluation harnesses for AI agents
- Familiarity with machine learning tooling and frameworks such as PyTorch, Hugging Face, or similar ecosystems
- Experience designing evaluation frameworks for NLP or speech models, including quality metrics and benchmarking
- Strong understanding of modern software engineering practices including testing, version control, API development, and CI/CD
- Experience deploying systems using Docker and containerised infrastructure
- Familiarity with GCP or Azure cloud platforms (both used)
- Comfortable working across the full stack of ML systems – from experimentation to production deployment
- Strong collaboration skills and ability to work with cross-functional teams
Nice to Have
- Experience working with clinical, medical, or regulated data environments
- Familiarity with real-time speech transcription systems
- Experience with ML observability, model monitoring, or evaluation tooling
- Background in AI safety, reliability engineering, or responsible AI