<p><strong>Robert Half is working with a client who is looking to hire a Machine Learning Engineer</strong> to help operationalize machine learning models as part of a growing enterprise AI program.</p><p>This role will sit between Data Science and Engineering and will focus on taking models from experimentation into reliable, scalable production environments.</p><p>Responsibilities</p><ul><li>Develop and deploy machine learning models into production.</li><li>Build reusable ML pipelines for training, testing, deployment, and monitoring.</li><li>Partner with Data Scientists to productionize predictive models.</li><li>Develop APIs and services that expose machine learning capabilities to enterprise applications.</li><li>Monitor model performance, drift, and reliability.</li><li>Build automated testing and deployment processes for ML workloads.</li><li>Optimize model performance and infrastructure utilization.</li><li>Work with Data Engineering teams to establish reliable training and inference datasets.</li></ul><p><br></p>

<p>Qualifications</p><ul><li>4+ years of software engineering, machine learning engineering, or related experience.</li><li>Strong Python development skills.</li><li>Experience with TensorFlow, PyTorch, Scikit-learn, or similar frameworks.</li><li>Experience deploying models into production environments.</li><li>Knowledge of Docker and Kubernetes.</li><li>Experience with AWS, Azure, or GCP machine learning services.</li><li>Understanding of ML lifecycle management and model monitoring.</li><li>Experience building REST APIs or microservices.</li></ul><p>Experience with Databricks, MLflow, SageMaker, Azure Machine Learning, or Vertex AI is highly desirable.</p>

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