Here's How You Make an Impact:
Develop & Deploy: Focus on the hands-on building, training, and operational deployment of machine learning models, ensuring they perform reliably within existing production environments.
Champion Technical Standards: Advocate for top-tier practices across coding, testing, and MLOps processes. Navigate ambiguity autonomously to refine pipelines and elevate ML engineering workflows.
Optimize & Scale: Construct resilient, cost-efficient ML & AI use cases. Balance sustaining established models with accelerating the rollout of highly scalable, modern systems.
Partner & Collaborate: Team up with cross-functional stakeholders, including risk specialists, product leads, and software developers, to convert strategic needs into technical specs and smoothly embed ML features into live applications.
Establish Controls & Governance: Uphold stringent benchmarks for model dependability, fairness, and compliance. Direct the integration of lineage tracking and data protection workflows into our automated systems.
Track & Evaluate: Formulate comprehensive observability systems to capture model health and key operational metrics, ensuring machine learning investments yield quantifiable organizational value.
You Thrive Here By Possessing the Following:
Experience: Minimum of 3–5 years of professional experience in machine learning engineering, with a proven track record of deploying models into production environments.
Technical Depth: Deep understanding of the modern data stack, including data ingestion workflows and experience working with curated data warehouses like Databricks or Redshift.
Cloud Proficiency: At least 3 years of hands-on experience with AWS infrastructure, specifically SageMaker, Spark/AWS Glue, and Infrastructure as Code (IaC), Terraform.
Orchestration Expert: High proficiency in managing multi-stage workflows using Airflow or similar orchestration systems to automate training and deployment cycles.
MLOps Toolkit: Practical experience with MLflow, Kubeflow, or SageMaker Feature Store to support the end-to-end machine learning lifecycle.
Governance Mindset: Familiarity with model governance practices (lineage, fairness, and privacy) and experience using data cataloging tools for compliance.
Communication: Strong ability to communicate complex technical concepts to non-technical stakeholders and influence project direction.
Industry Context: Experience in FinTech or Financial Risk environments is a significant advantage.
Skills Required
- 3-5 years professional experience in machine learning engineering with production deployments
- Experience with Databricks or Redshift
- At least 3 years hands-on AWS experience, specifically SageMaker
- Experience with Spark or AWS Glue
- Infrastructure as Code experience, specifically Terraform
- Proficiency with orchestration tools such as Airflow
- Practical MLOps experience with MLflow, Kubeflow, or SageMaker Feature Store
- Familiarity with model governance practices (lineage, fairness, privacy) and data cataloging tools
- Strong communication skills to explain technical concepts to non-technical stakeholders
- Experience in FinTech or Financial Risk
What We Do
Wave is one-stop money management for small business owners. We empower small business owners to manage their finances fearlessly by offering the simplest financial management solution they can’t live without. As an award-winning company, and one of the fastest-growing financial services software solutions, we are unwavering in our commitment to grow and support both our customers and our people. We care deeply and we challenge directly. We re-imagine the possible, together, to achieve extraordinary results for the small business owners we serve.
