We are looking for an AI/ML Platform Engineer to join the AI Platform team. This team builds and maintains Flo's shared platform for artificial intelligence, enabling every product team to use AI safely, efficiently, and at scale. If you're passionate about cutting-edge generative AI technologies and driven by building robust ML infrastructure that delivers value to millions of users, we would love to hear from you!

In this role, you will work at the intersection of machine learning engineering and MLOps, owning both the development and operationalization of AI/ML systems. Your responsibilities will span from fine-tuning and optimising large language models to building and maintaining the infrastructure that enables rapid experimentation and reliable deployment at scale. You will work with state-of-the-art technologies including LLMs, model evaluation frameworks, and modern ML infrastructure to build solutions that are medically safe and work at scale.

The AI Platform team acts as the central enabler of machine learning and AI initiatives across the organisation. Its mission is to reduce operational overhead and maximise ROI from ML use cases. The team builds and maintains critical infrastructure including LLM evaluation frameworks (AI Judges), model deployment pipelines, fine-tuning infrastructure, user profiles store, experiment tracking systems, and monitoring frameworks. By working closely with domain teams, the AI Platform team delivers scalable, high-quality solutions that accelerate time-to-market while ensuring compliance and maintaining the highest standards of performance.

What you'll be doing

• Develop, fine-tune, and optimise large language models for domain-specific health applications, working with both proprietary and open-source models (Gemini, GPT, Llama, etc)
• Design and maintain automated pipelines for model training, fine-tuning, evaluation, and deployment across diverse AI workloads
• Build and enhance LLM evaluation frameworks (AI Judges) for measuring model safety, medical accuracy, and performance
• Implement CI/CD practices for ML/AI engineering workflows, including experiment tracking, model versioning, and automated testing
• Orchestrate seamless deployment of models, AI agents, and inference endpoints with automated testing and rollback capabilities
• Implement comprehensive monitoring for model performance, drift detection, AI safety metrics, and responsible AI compliance
• Constantly improve technical capabilities by researching and implementing best practices in the rapidly evolving space of LLMs and generative AI
• Work in a cross-functional setup alongside other Flo Teams (Product, Security, Analytics, Marketing, Legal, etc)

Must have:

• 4+ years of professional experience in machine learning, with hands-on experience building and deploying production-grade AI/ML systems
• Recent engineering experience with LLM infrastructure and tooling, including fine-tuning (LoRA, SFT or other), prompt engineering, and model evaluation
• Strong Python programming skills for efficient model development, experimentation, and deployment
• Experience with modern ML infrastructure tools such as MLflow, experiment tracking systems, and model registries
• Databricks (or similar tooling) platform experience with Unity Catalog, MLflow, and Databricks Machine Learning for end-to-end AI/ML workflows
• Cloud platform expertise with one of the Hyperscalers (AWS, GCP, or Azure) including AI-specific services
• Understanding of the entire ML/LLM development lifecycle, including CI/CD, version control, testing, and agile methodologies
• Ability to devise creative solutions to intricate technical challenges, including experience in systems design with the ability to architect and explain ML/LLM pipelines
• Excellent communication skills and ability to collaborate with diverse teams
• Commitment to responsible AI practices, including fairness, accountability, and transparency

Nice to have:

• Experience with LLM fine-tuning techniques including LoRA adapters, preference optimisation (RLHF/DPO), and model distillation
• Containerisation and orchestration experience with Docker, Kubernetes, and ML-specific operators
• AI model serving experience with modern inference servers and API gateways for AI applications
• Infrastructure as Code experience with Terraform, Ansible, or other IaC tools
• Experience building and managing data pipelines using tools like Apache Spark
• AI safety and governance experience with model evaluation, LLM judges, bias detection, and responsible AI practices
• Familiarity with statistical fundamentals and running and evaluating experiments
• Experience in the MedTech or HealthTech domain
• Experience at a Tier-1 product company or related experience working within a product organisation

#LI-KP1 #LI-Hybrid