18 дней назад
Senior Machine Learning Engineer (MLOps)
Вакансия из Hirify Global, списка международных tech-компанийДля мэтча и отклика нужен Plus
Мэтч & Сопровод
Для мэтча с этой вакансией нужен Plus
Описание вакансии
Текст:
TL;DR
Senior Machine Learning Engineer (MLOps): Building feature stores, model deployment pipelines, Databricks experimentation environments, and low-latency serving infrastructure for demand forecasting across thousands of hospitality venues with an accent on production MLOps, model reliability, and real-time prediction delivery. Focus on designing feature and model lifecycle systems, operating serving APIs, productionising forecasting models, and building retrieval infrastructure for forecast explainability.
Location: Bondi Junction, New South Wales, Australia; hybrid workplace
Company
is a food-tech platform helping restaurants and bars fill empty tables, increase foot traffic, and improve revenue through real-time deals and data-driven technology.
What you will do
- Build and operate feature stores supporting demand forecasting, affinity modelling, and restaurant grouping across thousands of venues.
- Own model deployment pipelines, including versioning, rollouts, rollbacks, monitoring, and drift detection.
- Develop the Databricks-based experimentation environment in partnership with the Senior Data Scientist.
- Design serving APIs and low-latency infrastructure that deliver forecasts and recommendations to restaurant operators.
- Productionise forecasting models by benchmarking compute usage, latency, and cost.
- Build retrieval, vector database, and explainability infrastructure for variance attribution and the Conversational Venue Assistant.
Requirements
- Strong Python and production software engineering skills, including typed code, testing, CI/CD, and code review.
- Deep end-to-end MLOps experience covering model versioning, deployment, orchestration, experiment tracking, serving, monitoring, and drift detection.
- Hands-on experience with Databricks or an equivalent experimentation and production platform.
- Experience designing feature stores with online/offline consistency, freshness, and backfills.
- Strong AWS knowledge, API design skills, and backend engineering experience.
- Working knowledge of forecasting concepts such as quantile loss, exogenous regressors, and time-series cross-validation, plus strong communication with data scientists.
Nice to have
- Experience with LLM, RAG, or vector database infrastructure.
- Experience building a feature store from scratch or setting up an ML platform.
- Background in hospitality, retail, demand forecasting, or marketplace products.
- Broad experience across ML engineering, data engineering, data science, analytics, and software engineering.
Culture & Benefits
- Hybrid work with autonomy and flexibility.
- Collaborative environment focused on creativity, speed, ownership, and measurable production results.
- Opportunity to work directly with data science, backend, product, business development, and restaurant operators.
- Access to restaurants and hospitality leaders through a food-tech product used by millions of customers and thousands of venues.
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