AI/ML Engineer, Applied AI/ML

JOB SUMMARY: The AI/ML Engineer will collaborate closely with a team of skilled data scientists to develop innovative solutions that enhance guest experiences at our destinations and boost engagement with our digital products. This role requires a solid background in computer science, data engineering, machine learning, and technology. The engineer will be responsible for designing, developing, and maintaining large-scale AI/ML systems that improve guest experiences, increase relevancy, and drive operational efficiencies. Key responsibilities include ensuring smooth data integration into AI systems, orchestrating model selection, and automating AI/ML pipelines. The role also involves overseeing testing, monitoring, and optimizing ML pipelines in production while adhering to the highest standards in ML Ops and AI Ops. MAJOR RESPONSIBILITIES: • AI and ML Ops • Testing & Monitoring in Production: Implement continuous integration and deployment (CI/CD) for models. Test, monitor, and ensure the smooth operation of models in production environments, addressing issues proactively. • ML Ops & AI Ops: Ensure best practices in ML and AI operations, focusing on model versioning, reproducibility, scalability, and performance. • Collaboration: Work closely with data scientists, software engineers, and product teams to ensure successful integration of AI models into production systems. • Performance Tuning & Optimization: Continuously improve the performance of ML models and pipelines, ensuring they meet business and technical requirements. • Documentation & Compliance: Maintain comprehensive documentation for models, pipelines, and operational procedures. Ensure compliance with data security and privacy policies. • AI/ML Application Development • Design, implement, and automate end-to-end ML pipelines, from data preprocessing to model deployment and monitoring. • Build and maintain pipelines that efficiently integrate large datasets into ML/AI models, ensuring data is clean, relevant, and scalable. • Lead the model selection process, balancing between various LLMs and other AI models. Develop robust orchestration systems to deploy models efficiently. • Integrate models into AI-driven applications and workflows, optimizing them for performance and business use cases. • Data Enablement • Collaborate on building secure ETLs, application, data integrations, data workflows, data pipelines, API’s and automate data preparation jobs for analysis, consumptions, BI & reporting, delivering data to other application and platforms. Optimize data flow and processing performance, secure sharing, automated monitoring and alerts. • Engage with internal/external partners to gather requirements and collaborate with technology engineers, architects and data scientists to help in designing a robust data enablement solution. • Understand and actively participate in Environmental, Health & Safety responsibilities by following established UO policy, procedures, training, and team member involvement activities. • Perform other duties as assigned. EDUCATION: • Bachelor’s degree in Computer Science, Engineering, Data Science or in a relevant applied quantitative field is required. • Masters is preferred. EXPERIENCE: • 3+ years of hands-on experience in data engineering or data science, working with cross-functional teams to implement data and ML/AI solutions in real-world applications. ADDITIONAL INFORMATION: • Proficiency in Python, with expertise in ML frameworks like TensorFlow, PyTorch, or Keras, and experience writing production-level code for model training and deployment. • Expertise in cloud platforms for model deployment, scaling, and operationalization, with a preference for experience in serverless architecture and cloud storage solutions. • Familiarity with real-time data integration and streaming platforms, integrating streaming data sources into AI/ML workflows to enable real-time learning. • Proficiency in building and automating ML pipelines with the ability to orchestrate multiple models and data streams, automating from ingestion to deployment and monitoring. • Extensive experience with ML Ops/AI Ops frameworks for model monitoring, version control, and CI/CD, ensuring model accuracy and managing the production lifecycle. Strong experience with CI/CD tools for automating model deployment, testing, and version control in dynamic environments. • Knowledge of DevOps practices for AI/ML pipeline automation, including infrastructure-as-code, containerization, and orchestration. • Proven ability to work in cross-functional teams and translate busines