Senior Applied ML Engineer

About this role

N-iX is looking for a Senior Applied ML Engineer to support our client's Data Licensing team, which develops training datasets for frontier AI labs. You will evaluate and productionise existing ML models and services to deliver customer-requested metadata enrichment for data licensing opportunities, supporting the next generation of AI applications. Responsibilities: Partner with the Principal Data Scientist and Principal Data Engineers, senior technical leaders and data scientists to implement and evolve model-evaluation and productionisation standards; independently own assigned enrichment workflows from evaluation through production delivery.

Evaluate candidate models on representative content, using appropriate quality measures and threshold analysis to support selection and release decisions. Develop tested, maintainable Python inference components that conform to shared engineering frameworks, metadata contracts and deployment standards. Support ad-hoc metadata backfills by validating model outputs, estimating inference cost and throughput, and resolving model-specific processing issues.

Work with data engineers to move enrichment components from representative evaluation to reliable corpus-scale execution. Assess model licensing, data-handling constraints and provenance requirements, ensuring enrichment outputs are reproducible and suitable for commercial use Requirements: 6+ years of experience in applied ML, ML engineering, data science or software engineering, including hands-on experience delivering production ML inference systems.

Experience evaluating pretrained or third-party ML models, defining appropriate quality metrics and operating thresholds, while balancing model quality against inference cost, throughput and operational complexity. Strong Python software engineering skills, including testing, packaging and dependency management. Hands-on proficiency with ML libraries and frameworks such as PyTorch, TensorFlow, scikit-learn or Hugging Face.

Experience developing, deploying and maintaining high-performing, resilient and scalable ML inference systems across CPU and GPU environments in both AWS and GCP Experience troubleshooting and optimising model execution in distributed, cross-cloud workflows. Clear communication skills and experience building consensus with technical and commercial partners. Nice to have: Model tracking and lifecycle tooling such as MLflow.