Lead Assistant Manager

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    "ShortDescriptionStr": "MLOps Engineer\nLocation: Austin, TX- hybrid 3 days a week in office\nSalary: 110K-120K plus 10% bonus\nFor more information on benefits and what we offer please visit us at https://www.exlservice.com/us-careers-and-benefits \nThe posted range is the hiring range for this role- a subset of the broader range available to employees over time- and reflects base salary across our national hiring scale. ",
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    "ExternalDescriptionStr": "<p><span>We are looking for a motivated <strong>ML Engineer / MLOps Engineer</strong> with strong foundational experience in building and supporting machine learning systems in production environments. The ideal candidate will have hands-on exposure across the <strong>ML lifecycle</strong>, including data pipelines, model deployment, and monitoring, along with familiarity with cloud and ML Ops practices. This role involves working closely with senior engineers and data scientists to operationalize ML models for use cases such as <strong>personalization, recommendations, and NLP</strong>, while contributing to scalable and reliable ML solutions.</span></p>\n<p>&nbsp;</p>",
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    "ExternalQualificationsStr": "<ul>\n <li>2–4 years of experience in <strong>ML Engineering, Data Engineering, or MLOps</strong>, with exposure to end-to-end ML workflows. Proficiency in <strong>Python and SQL</strong>, along with hands-on experience or familiarity with ML frameworks such as <strong>Scikit-learn, TensorFlow, or PyTorch</strong>. Good understanding of <strong>machine learning concepts, evaluation techniques, and performance metrics</strong>, along with awareness of <strong>model monitoring, data drift, and model drift concepts</strong>.</li>\n <li>Experience or working knowledge of <strong>cloud platforms (AWS or GCP)</strong>, CI/CD tools (GitHub Actions, Jenkins), containerization (Docker), and orchestration (Kubernetes). Familiarity with <strong>MLflow, Feast, Airflow</strong>, and monitoring tools like Prometheus or Grafana is preferred.</li>\n <li>Strong problem-solving skills, willingness to learn, and ability to work in collaborative team environments. Bachelor’s degree in computer science, Engineering, or a related discipline preferred.</li>\n</ul>\n<p><span><strong>Nice-to-have:</strong> Exposure to <strong>real-time ML serving (KFServing, Seldon, Ray Serve)</strong>, A/B testing, or recommender systems. Understanding of <strong>experiment design or causal inference</strong>, and experience in <strong>media or subscription domains</strong>, will be an advantage.</span></p>",
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