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# Sr Machine Learning Engineer

- **Company:** Insight Global
- **Location:** Las Vegas, NV, United States
- **Experience:** Expert
- **Contract:** Permanent contract
- **Skills:** A/B Testing, Continuous Integration, Distributed Systems, Python (Programming Language), Machine Learning, Tensorflow, SQL Databases, Feature Store, Google Cloud, Pytorch, Large Language Models, Apache Spark, Pandas, Data Lakes, Scikit Learn, Kubernetes, Data Lineage, Optimization Algorithms, Deployment Automation, Machine Learning Operations, Databricks
- **Published:** October 7, 2026
- **Apply:** https://www.lasvegasjobsite.com/job.asp?id=3423491916&tx=ZT212TYI&pt=1&aff=0B19D771-A501-4A5E-8338-2A822B784D54&utm_source=Job%20Feed&utm_medium=textkernel&utm_campaign=DE&utm_term=0B19D771-A501-4A5E-8338-2A822B784D54

## About the Role

* 5+ years of experience building, scaling, and deploying machine learning solutions and pipelines using Python, Databricks, GCP, Spark, Delta Lake, Kubernetes, CI/CD, and automated deployment workflows.
 * Strong expertise in statistical and quantitative analysis, predictive modeling, anomaly detection, time-series forecasting, demand prediction, experimentation, A/B testing, online evaluation, and optimization algorithms.
 * Proven experience managing the end-to-end ML lifecycle, including MLflow, feature stores, model registries, lineage tracking, model monitoring, drift detection, automated retraining, model rollout strategies, and governance in regulated environments.
 * Advanced programming and machine learning expertise with Python, SQL, Spark, Pandas, TensorFlow, PyTorch, scikit-learn, hyperparameter optimization, experiment tracking, and scalable distributed computing architectures.
 * Experience applying modern AI techniques, including foundation models, embeddings, vector databases, retrieval-augmented approaches, and generative AI, with the ability to translate business requirements into production-ready ML solutions.

## Description

This role joins a lean, high-impact Enterprise Analytics team focused on building and operationalizing machine learning solutions across a casino organization. The environment is highly hands-on, with the ML Engineer expected to design, build, deploy, and support production-grade ML solutions while partnering closely with Data Scientists and business stakeholders. The ideal candidate is comfortable owning projects end-to-end and thrives in an environment where initiative, communication, and independent problem-solving are critical.