Senior Machine Learning Engineer – Contract

• Refactor, modernise and productionise existing ML models and Applied AI capabilities, including NLP and generative AI solutions
• Build new ML components and re-engineer existing models into standardised, production-ready modular components
• Develop production ML applications and supporting services primarily using Python
• Build and maintain reliable ML pipelines for model integration, evaluation, deployment and operation
• Engineer resilient ML workflows with retry logic, error handling and repeatable execution
• Design and automate model evaluation pipelines using golden datasets and quality/performance thresholds
• Evaluate generative AI, classification and other ML models using appropriate metrics
• Implement guardrails and evaluation mechanisms for grounding, hallucinations and generative AI output quality
• Apply Applied AI techniques, including RAG
• Design model, prompt and input-data provenance mechanisms for auditability and reproducibility
• Build infrastructure for shadow testing, A/B testing, fallback strategies and kill switches
• Support labelling, curation and ongoing development of golden datasets
• Build human-in-the-loop feedback pipelines to capture reviews and corrections
• Integrate third-party AI APIs and build adapter/API interfaces
• Implement observability and telemetry for model behaviour, errors, compute costs, token usage and latency
• Contribute backend engineering to integrate ML components into the wider application
• Support batch and real-time ML workloads

• 6+ years of professional AI/Machine Learning experience, with genuine production experience
• 5+ years of professional MLOps experience
• At least 2+ years of real Applied AI experience beyond experimentation or personal projects
• Strong professional Python experience
• Proven experience productionising and deploying AI/ML applications and models
• Strong understanding of Applied AI/ML and MLOps
• Strong hands-on experience with model evaluation and defining quality/performance criteria
• Experience with generative AI/LLMs, including grounding and hallucination evaluation
• Hands-on understanding of RAG and other Applied AI techniques
• Experience building and operating ML pipelines and production ML architectures
• Experience designing reliable ML workflows with error handling, retry mechanisms and repeatable execution
• Experience with golden datasets for model evaluation and quality gating
• Experience building observable ML systems using logging, monitoring and telemetry
• Understanding of model/data provenance, auditability and reproducibility
• Experience implementing safe production deployment practices, including testing, fallback or fail-safe mechanisms
• Sufficient backend engineering experience to build APIs, integrations and production-ready services around ML capabilities
• Experience solving real production ML problems involving reliability, deployment, integration, evaluation or performance
• Familiarity with governance, compliance and safeguards for sensitive data and AI-generated outputs
• Nice to have: FastAPI
• Nice to have: Argo Workflows or similar DAG-based orchestration frameworks
• Nice to have: Docker and Kubernetes
• Nice to have: AWS, Azure or GCP
• Nice to have: Multi-cloud or cloud-agnostic application experience
• Nice to have: TypeScript and/or Go
• Nice to have: Speech-to-text or transcription models
• Nice to have: Real-time ML applications
• Nice to have: Traditional NLP models, transformer-based models, encoders and decoders
• Nice to have: External model/provider integrations such as OpenAI or Claude

• Remote work arrangement
• Genuine community focused on growth and well-being
• Long-term role
• Professional and personal growth opportunities