Waveapps
Machine Learning Engineer II
Remote
Full Time
#AI
#Machine Learning
#AWS
#Sagemaker
#Airflow
#Terraform
#MLFlow
#Kubeflow
#Snowflake
#Databricks
#Redshift
#MLOps
Waveapps is looking for a Machine Learning Engineer II
Full TimeSeniorRemoteAIMachine LearningAWSSagemakerAirflowTerraformMLFlowKubeflowSnowflakeDatabricksRedshiftMLOpsEnglishRemote work
At Wave, we help small businesses thrive so the heart of our communities beats stronger. We work in an environment full of creative energy and inspiration, where you have the tools and support you need to succeed no matter where you are. As a Machine Learning Engineer II on a full-time remote basis, you will play a central role in shaping the design, development, and deployment of our core AI and ML models. You will create reliable, scalable machine learning pipelines and platforms that power advanced analytics and business intelligence across the organization.
Responsibilities
- Design and implement modern AI stack components, including data ingestion for AI/ML workloads and complete pipelines for model training and serving.
- Build and manage fault-tolerant AI platforms that scale efficiently while balancing the upkeep of existing models with the creation of new, advanced solutions.
- Mentor junior engineers, foster collaboration between data science and production teams, and promote best practices in coding, testing, and MLOps to ensure measurable business outcomes.
Requirements
- Four to six years of professional experience in machine learning engineering with a demonstrated record of deploying models into production environments.
- A degree or diploma in Computer Science, Engineering, Data Science, Applied AI, Machine Learning, or a related field.
- Strong understanding of the modern AI stack, including data ingestion workflows and hands-on work with data warehouses such as Snowflake, Databricks, or Redshift.
- At least three years of practical experience with AWS infrastructure, including SageMaker, Spark or AWS Glue, and Infrastructure as Code using Terraform.
- Proficiency in orchestrating multi-stage workflows with Airflow or similar tools to automate training and deployment cycles.
- Experience with MLflow, Kubeflow, or SageMaker Feature Store to manage the full machine learning lifecycle.
- Familiarity with model governance practices such as lineage, fairness, and privacy, along with the ability to communicate complex technical concepts to non-technical stakeholders.
- Experience in FinTech or SaaS environments is considered an advantage.
What we offer
- Fully remote work arrangement that supports flexibility and connection regardless of location.
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