Overview

Our mission is simple: to break down geographic barriers and connect skilled professionals with outstanding global teams and products for full-time, long-term roles.

Responsibilities

• 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 covering model integration, evaluation, deployment and operation.
• Design and automate model evaluation pipelines using golden datasets and appropriate quality and performance thresholds.
• Implement appropriate guardrails and evaluation mechanisms to assess grounding, hallucinations and quality of generative AI outputs.
• Design mechanisms for model, prompt and input-data provenance to support auditability and reproducibility.
• Build infrastructure supporting shadow testing, A/B testing, fallback strategies and kill switches for safe ML deployment.
• Support the labelling, curation and ongoing development of golden datasets used for model evaluation.
• Build structured human-in-the-loop feedback pipelines to capture reviews and corrections and improve ML datasets.
• Integrate third-party AI APIs and build appropriate adapter/API interfaces.
• Implement observability and telemetry covering model behaviour, errors, compute costs, token usage and latency.
• Support both batch and real-time ML workloads as the platform develops.

Qualifications

• 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, working with AI/ML capabilities beyond experimentation or personal projects.
• Strong professional Python experience; Python is the core programming language for this role.
• Proven experience productionising and deploying AI/ML applications and models.
• Strong understanding of both Applied AI/ML and MLOps, rather than experience limited solely to model research or experimentation.
• Strong hands-on experience with model evaluation and defining appropriate quality/performance criteria for production ML systems.
• Hands-on understanding of RAG and other Applied AI techniques.
• Understanding of model/data provenance, auditability and reproducibility.
• Sufficient backend engineering experience to build APIs, integrations and production-ready services around ML capabilities.
• Familiarity with governance, compliance and safeguards relating to sensitive data and AI-generated outputs.
• Exposure to Argo Workflows or similar DAG-based orchestration frameworks.
• Multi-cloud or cloud-agnostic application experience.
• • Production
• experience with speech-to-text or transcription models. •
• We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses and identifying potential inconsistencies or verification signals in application materials based on available information. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed, please contact us.