AI / ML Engineer

Responsibilities:

• Define AI/ML architecture, model-selection strategy, reusable service patterns and technical standards across products.
• Lead the design of LLM workflows, retrieval systems, tool use, routing, structured outputs and human-review controls.
• Guide predictive-model development for classification, ranking, recommendation, forecasting and anomaly detection.
• Establish dataset, experimentation, offline evaluation, online measurement and model-releasepractices.
• Define quality, safety, latency and cost thresholds and ensure model changes are validated before production release.
• Design MLOps and LLMOps capabilities covering versioning, deployment, tracing, drift, rollback and provider resilience.
• Review AI designs and code, create reference implementations and resolve complex modelling or production issues.
• Mentor AI/ML engineers, improve technical depth and coordinate delivery with backend, frontend, QA, security and cloud teams.
• Assess external models and platforms while protecting data, limiting vendor dependence and controlling operational cost.
• Communicate evidence, uncertainty, model limitations and technical trade-offs clearly to leadership and delivery teams.

Requirements:

• Advanced Python and strong foundations in machine learning, deep learning, statistics and experimental design.
• Proven ownership of production LLM applications, including structured outputs, tool calling, retrieval and evaluation.
• Experience delivering classical ML systems using scikit-learn, PyTorch, TensorFlow or comparable frameworks.
• Strong understanding of precision, recall, ranking metrics, calibration, drift, bias, data leakage and error analysis.
• Experience with model deployment, versioning, feature or data pipelines, observability, and rollback.
• Ability to architect secure AI services and defend against prompt injection, unsafe tool use and sensitive-data leakage.
• Strong SQL and data-analysis skills with experience working on event, behavioural, or time-series datasets.
• Technical leadership, mentoring, design review and stakeholder communication experience.

Preferred experience:

• Multi-model routing, agent orchestration, vector retrieval and knowledge-graph experience.
• Causal inference, uplift modelling, recommendations, optimisation or contextual bandits.
• Multimodal models involving text, image, audio or video.
• Cloud-based ML platforms, Kubernetes, distributed inference and GPU workload optimisation.
• Experience building shared AI platforms or standards used by more than one product team.