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About the Role
As a Senior ML Engineer, you will bridge the gap between applied machine learning and robust platform engineering. You will focus on translating AI capabilities into reliable and observable system components. You will help ensure that machine learning models meet strict quality, cost and latency thresholds before reaching production.
The role
spans production ML architecture, workflow reliability, model evaluation, provenance, testing, human feedback integration and AI provider observability.
Responsibilities
Refactor and upgrade existing ML models, including NLP, generative AI and transcription models, into standardised, production-ready modular contracts.
Engineer resilient ML workflows using DAG-based orchestration tools such as Argo Workflows and Airflow.
Ensure robust retry logic, error handling and repeatable execution across ML workflows.
Define, automate and maintain strict ML evaluation pipelines using golden datasets.
Ensure new models meet baseline quality and performance thresholds before release.
Design and implement tracking mechanisms to capture precise model, prompt and input data provenance for full auditability and reproducibility.
Build infrastructure for shadow testing and A/B testing on live traffic.
Implement reliable fallback and kill-switch mechanisms to support safe deployment.
Engineer structured feedback pipelines that capture human reviews and corrections to continuously enrich and refine training datasets.
Integrate third-party AI APIs and manage adapter interfaces.
Implement granular telemetry to track compute costs, token usage and latency.
Requirements
Proven track record operating at the intersection of Machine Learning, MLOps and Platform/Backend Engineering.
Deep understanding of evaluation metrics for generative AI, LLMs and/or speech models, including Precision, Recall, F1, WER/CER, groundedness and hallucination rates.
Strong hands-on experience with containerisation using Docker and Kubernetes, alongside modern orchestration frameworks.
Experience building observable ML systems with logging, monitoring and strict cost-attribution tracking.
Familiarity with data provenance, compliance standards and building fail-safe mechanisms for sensitive data and AI outputs. 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.