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
- Lead ML Engineer developing scalable data pipelines and ML systems for Newfold Digital. Collaborate with cross-functional teams using Python, SQL, and cloud ML platforms in an applied ML environment.
Responsibilities
- Partner with the business to translate requirements into clear problem statements, KPIs, and experiment plans (A/B, holdout, backtests)
- Design data & ML architectures on lakehouse /warehouse stacks (e.g., Oracle Exadata, Spark/Databricks; Snowflake / BigQuery /Redshift with open table formats like Iceberg/Delta/Hudi or equivalent)
- Build pipelines for ingestion, feature engineering, and training (batch & streaming) using Python + SQL with orchestration (Airflow/Prefect/Dagster)
- Model using scikit-learn/XGBoost/LightGBM and PyTorch/TensorFlow; manage experiments and lineage
- Serve & operate models on a major cloud ML platform (Azure ML, SageMaker or Vertex AI), with CI/CD, canary/blue-green, and rollback guardrails
- Monitor & improve: implement data/model quality and drift monitoring, alerting, and dashboards; close the loop with BI (Power BI/Tableau/Looker)
- Document & review: author concise design docs and run technical reviews; mentor engineers; champion responsible AI practices
Requirements
- 8+ years in applied ML & data engineering (3+ years leading delivery of production ML systems)
- Python expert with production-grade SQL; strong with pandas/Polars, scikit-learn, and one of: XGBoost/LightGBM
- Fluency in core ML toolkits including TensorFlow, PyTorch, scikit-learn, and familiarity with Hugging Face or equivalent frameworks
- Proven record of constructing and maintaining scalable data pipelines—both batch and streaming—for model training and deployment
- Data platforms: hands-on with one of: Oracle ExaData, Spark/Databricks, or Snowflake, BigQuery/Redshift or equivalent; comfortable with open table formats (Iceberg/Delta/Hudi)
- Orchestration: real projects using one of Airflow, Prefect, or Dagster
- Cloud ML platform: production deployments on one of SageMaker, Vertex AI, or Azure ML (pipelines, endpoints, registries)
- MLOps: CI/CD for ML, experiment tracking, model registry, observability (latency, errors), and data/model drift monitoring
- Communication: ability to frame trade-offs and influence cross-functional partners; crisp writing of design/decision docs
Benefits
- Flexible work arrangements
- Professional development
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