Here's How You Make an Impact:
Design & Execute: Take ownership of the design and implementation of modern AI stack components, including data ingestion for AI/ML workloads and end-to-end model training and serving pipelines.
Scale & Optimize: Build and manage fault-tolerant AI platforms that scale economically. You will balance the maintenance of legacy models with the rapid development of advanced, scalable solutions.
Mentor & Collaborate: Provide technical mentorship to junior engineers and foster a collaborative environment. You will act as a bridge between data science and production engineering.
Drive Technical Excellence: Promote best practices in coding, testing, and MLOps. You thrive in ambiguous conditions by independently identifying opportunities to optimize model pipelines and improve AI workflows.
Cross-Functional Integration: Partner with data scientists, product managers, and software engineers to translate business needs into technical requirements and integrate AI solutions into production applications.
Implement Governance: Enforce model quality standards, integrity, and reliability. You will be responsible for implementing model lineage, fairness, and privacy controls within the automated pipelines.
Monitor & Measure: Build monitoring frameworks to track model performance and system KPIs, ensuring our AI initiatives drive measurable business outcomes.
You Thrive Here By Possessing the Following:
Experience: Minimum of 4–6 years of professional experience in machine learning engineering, with a proven track record of deploying models into production environments.
- Education: Degree/Diploma in Computer Science, Engineering, Data Science, Applied AI, Machine Learning, or some combination.
Technical Depth: Deep understanding of the modern AI stack, including data ingestion workflows and experience working with curated data warehouses like Snowflake, 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) using 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 SaaS environments is a significant advantage.
Skills Required
- 4-6 years of professional machine learning engineering experience
- Proven track record of deploying models into production environments
- Degree or diploma in Computer Science, Engineering, Data Science, Applied AI, Machine Learning or equivalent
- Experience with curated data warehouses such as Snowflake, Databricks, or Redshift
- At least 3 years hands-on AWS experience, specifically SageMaker and Spark/AWS Glue
- Infrastructure as Code (IaC) experience using Terraform
- High proficiency with workflow orchestration (Airflow or similar)
- Practical experience with MLflow, Kubeflow, or SageMaker Feature Store
- Familiarity with model governance practices (lineage, fairness, privacy) and data cataloging tools
- Ability to communicate complex technical concepts to non-technical stakeholders
- Experience mentoring or providing technical guidance to junior engineers
- Experience in FinTech or SaaS environments
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.
