At Comply, we are building cutting-edge solutions that help prevent money flowing to and from bad actors to create a safer world. Your work will allow our customers to find out who is associated with crimes, financial and political risks, what that association is, and when it occurred, and it will dynamically update their state as new information emerges across a huge range of sources. What you will be doing
As a Machine Learning Engineer, you will:
Build/train and productionize machine learning models for your squad
Build capabilities to monitor model performance and feature drift
Where appropriate re-use public models and techniques such as prompt engineering and RAG to reduce time to value
Collaborate with other software engineers in a cross functional team to design and implement intelligent services
Design software with scale, transparency and ease of operation in mind, writing maintainable, performant and well-tested code in Python
Integrate ML models into new and existing data pipelines to drive positive impacts for CA’s customers, including feature engineering as well as building APIs and consuming and producing event streams as inputs and outputs of models
Our Tech Stack:
Our technology stack is designed to run on public cloud architectures, notably AWS and GCP
We use Python and Kotlin in the backend and TypeScript, ES6 and React in the frontend
We make substantial use of modern database technologies (eg. Yugabyte) as well as Spark and cloud based object stores for big data processing
We also use an event-sourced model powered by Kafka for our communication bus and gRPC for our intra-service communication protocol
Our data and AI teams use a wide range of machine learning libraries, large scale hybrid columnar data stores such as Databricks, Spark for stream and big data processing in combination with Kafka, as well as some graph databases
We use modern observability solutions (Grafana) and deploy our code using ArgoCD
We have a strong emphasis on engineering excellence and strive to ship the best possible code and the best possible solutions to our customers. About you:
Experience maintaining and scaling machine learning-powered applications in production environments. - Experience building scalable backend applications (preferably with Python). - A track record of working in multi-disciplinary teams alongside Data Scientists, ML Engineers, and Product Managers. - Hands-on experience with cloud platforms (AWS, Azure, or GCP) or with containerized infrastructure (e.g., Kubernetes, Docker, ArgoCD, Argo Workflows). - Strong communication skills and a collaborative mindset, with the ability to contribute to system design discussions and mentor more junior engineers.
