Freelance ML Engineer – Add Model to Our SageMaker Workflow

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We are looking for a freelance AWS SageMaker engineer to support an enhancement to our existing ML pipeline. Our current setup runs machine learning models in SageMaker and produces client-facing output through a semi-automated process. We now need to add an additional ML model to this pipeline and ensure its output is integrated cleanly with our existing results. The ideal candidate should have hands-on experience with SageMaker pipelines and be comfortable working with Python-based workflows. You’ll be responsible for inserting a new model into the current sequence, ensuring compatibility with data inputs and outputs, and updating any post-processing logic as needed. This is not a ground-up build—we have a functioning SageMaker-based architecture, and the goal is to enhance it, not redesign it. You should be comfortable reading existing code, understanding how it’s wired together, and making focused updates. Experience with other AWS services like S3, Lambda, and CloudWatch is helpful but not required. You will not be responsible for training the model, only integrating and supporting its output within the system. We will provide access to the relevant codebase, and support for context and testing. We’re a startup, so we value speed, clean code, and proactive communication. If you’re someone who can jump in quickly and deliver independently, we’d love to hear from you. Please include examples of similar work you’ve done, ideally in SageMaker or other AWS-based ML environments.

Python Machine Learning AWS Application