Our Client is seeking a hands-on Machine Learning Engineer to help build and operationalize ML capabilities supporting a learning opportunity-assessment solution. This role will translate ML and analytical strategies into repeatable feature pipelines, ML workflows, model lifecycle processes, and data integrations.
Key Responsibilities:
- Build and maintain feature-engineering and ML pipelines
- Develop repeatable workflows for data preprocessing, model training, evaluation, inference, and retraining
- Operationalize ML models beyond experimentation into scalable execution processes
- Implement model versioning, experiment tracking, monitoring, testing, and reproducibility practices
- Develop data ingestion and integration workflows across enterprise environments
- Partner closely with Data Scientists and technical stakeholders to move ML solutions from concept to implementation
Requirements
- Strong hands-on Python and SQL development
- Experience building ML and feature-engineering pipelines, beyond traditional ETL/data engineering
- Experience with Scikit-learn or comparable ML frameworks
- Strong understanding of features, model training, evaluation, inference, and operationalization
- Experience with structured and unstructured data processing using Pandas, NumPy, or similar libraries
- Experience with model registries, versioning, experiment tracking, monitoring, automated testing, and retraining workflows
- Experience with cloud-based ML/data environments, APIs, integrations, and version-control practices
- Strong software engineering fundamentals and experience developing production-oriented ML solutions
Preferred Skills:
- MLflow or similar lifecycle tooling, Feature Stores, containerization, cloud ML platforms, NLP/text-processing pipelines, automated ML testing, enterprise data platforms, and large-scale enterprise ML experience
- Ideal Profile: A hands-on ML engineer who has successfully moved models beyond notebooks into reliable, repeatable, operational ML workflows
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