Machine Learning Engineer

A technology company is embedding AI into its core product workflows - predictive models, intelligent automation, and LLM-enabled features - and needs an engineer who can build and own these systems in production. This is a hands‑on role at the intersection of applied ML and platform engineering. You’ll be setting the standard for how AI is built and operated here.

What you'll do Design, build, and deploy production ML models - REST inference services, batch pipelines, real-time scoring Establish MLOps practices: model versioning, monitoring, alerting, retraining, and lifecycle governance Evaluate new AI use cases and select the right approach - supervised learning, embeddings, retrieval-driven architectures, or hybrid Integrate ML outputs into product workflows alongside application engineering teams Partner with Data Engineering to ensure AI‑ready data pipelines and structures Work with external AI partners initially, then progressively take ownership in-house What you need 7+ years in ML engineering or applied ML, with production‑grade systems in enterprise environments Strong Python and ML library experience - scikit‑learn, PySpark, pandas Solid MLOps background: MLflow or equivalent model lifecycle tooling Cloud‑native experience - Azure preferred; AWS or GCP considered Pragmatic approach to modelling - picks the right tool for the problem, not the most fashionable one Experience with ML and backend development tools: Python, MLflow / MLOps, Azure, scikit‑learn / PySpark, REST, inference, APIs, Model monitoring, LLMs Nice to have Databricks - for scalable training pipelines and unified data + ML workflows Vector search, embeddings, or retrieval‑augmented generation (RAG) experience Hospitality, travel, or similarly data‑rich consumer industry background #J-18808-Ljbffr