Senior Machine Learning Engineer

Title: : Senior Machine Learning Engineer

Location: Las Vegas, Nevada (hybrid 3days to office)

Duration: 06+ Months

Position Summary:

Qualifications:

Position Overview:

The Senior Machine Learning Engineer is responsible for designing, building, and operationalizing advanced machine learning systems that support critical business functions across The Client. This role serves as a technical expert and thought leader who develops scalable ML and AI solutions, architects production-grade pipelines, and ensures models are deployed, monitored, and maintained with enterprise reliability. The ideal candidate brings deep experience in machine learning engineering, cloud technologies, and large-scale data processing, combined with strong skills in statistical modeling, MLOps practices, and modern AI methodologies.

They will partner closely with Data Engineering, Operations, and business stakeholders to translate complex business challenges into scalable solutions that drive operational efficiency and strategic decision-making. The Senior Machine Learning Engineer works autonomously, mentors other team members, and champions best practices in model governance, Responsible AI, security, and documentation. A passion for innovation, problem solving, and continuous learning is essential. All duties are to be performed in accordance with departmental and The Client policies, practices, and procedures.

Essential Duties & Responsibilities

• Architect and build scalable cloudbased data and ML pipelines, as well as a robust ML framework to support model training, deployment, inference, and monitoring at scale.
• Lead the design, development, evaluation, validation, and implementation of machine learning models aligned to business objectives.
• Conduct data preprocessing, feature engineering, exploratory data analysis, and deep dives to uncover trends and support model development and business insights.
• Manage and optimize endtoend ML workflows, including data ingestion, orchestration, and pipeline reliability.
• Implement comprehensive model monitoring, including performance tracking, drift detection, data quality checks, and automated retraining triggers.
• Design and implement predictive analytics solutions, experiments, and model algorithms to improve forecasting, optimization, and operational decisionmaking.
• Incorporate clear and effective data visualization techniques for both technical and nontechnical audiences.
• Make informed infrastructure and modeling decisions, including model selection, feature strategies, hyperparameter tuning, and evaluation methodologies.
• Develop and maintain detailed documentation for operational readiness and crossteam alignment.
• Ensure code quality, security, and compliance; maintain ML governance best practices, including Responsible and Explainable AI standards.
• Lead code reviews and provide technical guidance, mentorship, and bestpractice reinforcement across the team.
• Stay current with industry trends, emerging research, and new technologies to drive continuous improvement and innovation in ML engineering.

Minimum Qualifications

• Bachelor s degree in computer science, engineering, data science, statistics, mathematics, or a related field (Master s preferred).
• Minimum of 5+ years of relevant ML engineering experience.
• Handson experience building, scaling, and deploying ML pipelines in Python, preferably within Google Cloud Platform and Databricks.
• Strong programming and data manipulation skills (Python, SQL, Spark, Pandas), with experience in machine learning frameworks (TensorFlow, PyTorch, scikitlearn) and optimization tools.
• Experience with CI/CD, Git, and automated ML deployment workflows.
• Demonstrated experience in statistical/quantitative analysis, forecasting, predictive modeling, anomaly detection, experimentation, and optimization algorithms.
• Expertise designing and developing ML systems, including distributed computing architectures (Spark, Delta Lake, Kubernetes).
• Familiarity with MLflow, feature stores, model registries, and lineage tooling.
• Experience implementing robust model monitoring, including performance tracking, drift detection, and automated retraining.
• Experience with A/B testing frameworks, online evaluation, and model rollout strategies.
• Experience designing ML governance workflows in regulated environments.
• Strong understanding of ML fundamentals, ability to translate business requirements into scalable ML solutions, and experience in hyperparameter optimization and experiment tracking.
• Ability to work with modern ML techniques, including foundation models, embeddings, vector databases, retrievalaugmented ML approaches, and generative AI where relevant.
• Strong understanding of timeseries forecasting, demand prediction, and optimization algorithms.
• Excellent analytical, problemsolving, communication, and crossfunctional collaboration skills.
• Ability to explain complex technical concepts in clear, simple terms for diverse business audiences.

Physical Requirements

Must be able to:

• Physically access all areas of the property and drive areas with or without reasonable accommodation.
• Maintain composure under pressure and consistently meet deadlines with internal and external customers and contacts.
• Ability to interact appropriately and effectively with guests, management, other team members, and outside contacts.
• Ability for prolonged periods of time to walk, stand, stretch, bend and kneel.
• Work in a fast-paced and busy environment.
• Work indoors and be exposed to various environmental factors such as, but not limited to, CRT, noise, dust, and cigarette smoke.