The Machine Learning Engineer is responsible for taking predictive models from research prototypes into production-ready systems. In this role, you will collaborate with Data Scientists to refine algorithms, build scalable data pipelines, and deploy models that deliver real‐time or batch predictions. You will ensure that models perform reliably in production, monitor their behavior over time, and iterate to maintain accuracy as data evolves.

Working closely with Software Engineers, DevOps teams, and product managers, the Machine Learning Engineer translates business requirements into technical solutions. This includes selecting appropriate model architectures, optimizing inference performance, and integrating models into existing applications or APIs. A strong focus on automation, reproducibility, and code quality underpins every stage of the machine learning lifecycle.

Key responsibilities include:

• Model Development & Optimization: Implement, train, and fine‐tune machine learning models (e.g., regression, classification, deep learning) using frameworks such as TensorFlow, PyTorch, or scikit-learn
• Data Pipeline Engineering: Design and maintain data ingestion and transformation workflows with tools like Apache Airflow, Spark, or custom ETL scripts
• Deployment & Scaling: Containerize models with Docker, orchestrate deployment via Kubernetes or serverless platforms, and automate CI/CD for ML artifacts
• Performance Monitoring: Build monitoring dashboards (Prometheus, Grafana) and implement automated alerts for model drift, latency spikes, and prediction accuracy degradation
• Collaboration & Documentation: Work with cross‐functional teams to define feature contracts, write clear API specifications, and maintain comprehensive documentation for data schemas and model behavior
• Research Integration: Stay abreast of the latest ML research and evaluate new techniques—NLP, computer vision, reinforcement learning—for potential application to product challenges

Success in this role is measured by the reliability and accuracy of models in production, the speed at which new models can be deployed, and the degree to which machine learning solutions drive measurable business impact (e.g., improved user engagement, cost savings, or revenue growth).

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