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
- Machine Learning Engineer developing production AI models and scalable MLOps platforms for Wave, which helps small businesses thrive. Collaborating on financial-risk applications, governance, observability, and reliable deployment.
Responsibilities
- Design, develop, train, and deploy foundational AI and machine learning models in production environments
- Build robust, scalable machine learning pipelines and platforms supporting advanced analytics and business intelligence
- Advocate for high standards across coding, testing, and MLOps processes
- Construct resilient, cost-efficient ML and AI use cases and scale modern systems
- Collaborate with risk specialists, product leads, and software developers to translate strategic needs into technical specifications and embed ML features into live applications
- Establish model dependability, fairness, compliance, lineage tracking, and data protection controls
- Develop observability systems to capture model health and operational metrics and evaluate organizational value
Requirements
- Minimum 3–5 years of professional experience in machine learning engineering
- Proven track record of deploying machine learning models into production environments
- Deep understanding of the modern data stack and data ingestion workflows
- Experience with Databricks or Redshift
- At least 3 years of hands-on experience with AWS infrastructure, including SageMaker, Spark/AWS Glue, and Terraform
- High proficiency with Airflow or similar orchestration systems
- Practical experience with MLflow, Kubeflow, or SageMaker Feature Store
- Familiarity with model governance practices, including lineage, fairness, and privacy
- Experience using data cataloging tools for compliance
- Strong ability to communicate complex technical concepts to non-technical stakeholders and influence project direction
- FinTech or Financial Risk experience is a significant advantage
Benefits
- Bonus Structure
- Employer-paid Benefits Plan
- Health & Wellness Flex Account
- Wellness Days
- Paid Holiday Shutdown
- Wave Days (extra vacation days in the summer)
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