MLOps Engineer building production ML infrastructure, pipelines, and monitoring for exacare ai’s post - acute care AI platform. Scaling reliable systems that help healthcare teams make safer placement decisions.
About the role
- Senior Data Engineer building cloud-native data and ML platforms for Hive.co’s event-marketing automation products. Owning pipelines, model infrastructure, and audience-data systems at scale.
Responsibilities
- Design and own a cloud-native big data platform handling audience data for millions of attendees and billions of interactions annually
- Design and own ML infrastructure including feature stores, training pipelines, model serving, and monitoring
- Ensure reliable, low-latency access to features and ML infrastructure
- Own the full data pipeline from change data capture through validation, transformation, and denormalization
- Connect data-system performance and reliability to customer and business outcomes
- Build data products with defined SLAs, strong data health, and discoverability
- Use AI coding agents such as Claude Code
- Build LLM-powered pipelines and autonomous agents to enrich, classify, and act on audience data at scale
- Collaborate with product and engineering teams and communicate technical decisions to stakeholders
Requirements
- 8+ years of hands-on data engineering experience
- Proven track record designing, building, and operating large-scale distributed data and ML systems in production
- Core ML foundations, including supervised/unsupervised learning, cross-validation, bias–variance, regularization, evaluation metrics, regression, tree ensembles, and clustering
- Feature engineering with Python ML tooling, including pandas and scikit-learn; familiarity with PyTorch or TensorFlow
- Experience with production ML pipelines and feature datasets for model training and inference
- MLOps practices: experiment tracking, model versioning/registry, deployment, and monitoring for drift/data quality
- Strong distributed systems foundations: partitioning strategies, consistency models, backpressure handling, fault tolerance, and capacity planning
- Experience applying LLMs and agentic systems in production data or ML contexts
- Product and commercial orientation with ability to frame technical decisions in terms of customer impact and business outcomes
- Stakeholder communication skills for non-technical audiences
- Programming experience with Python and Django
- Experience with Clickhouse, MySQL, MongoDB, ElasticSearch, and Redshift
- Experience with Airflow or Dagster
- Comfortable operating independently in ambiguous, fast-changing environments
- Skilled at troubleshooting complex ML systems
- Nice to have: owning or re-architecting a data platform end-to-end
- Nice to have: background in SaaS or event-driven products
Benefits
- Meaningful salary and equity
- Work fully remote from the comfort of your home
- Flexible work hours: minimal meetings and no 9-5
- Health & Dental coverage with Parental Leave top-ups in addition to EI benefits
- Unlimited vacation/PTO
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