AI/ML Engineer, Applied AI/ML

Universal Orlando•Orlando, FL

About The Position

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.

Requirements

Bachelor’s degree in Computer Science, Engineering, Data Science or in a relevant applied quantitative field is required.
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.
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 business needs into AI/ML solutions, communicating technical concepts to non-technical stakeholders.
Strong communication skills to effectively present AI/ML insights and collaborate with internal and external teams.
Experience defining and tracking success metrics for AI/ML initiatives and aligning them with business objectives.
Stay current with AI/ML advancements, particularly in LLMs, and apply new methods to improve model performance.

Nice To Haves

Masters is preferred.

Responsibilities

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.
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.
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.
Understands and actively participates in Environmental, Health & Safety responsibilities by following established UO policy, procedures, training and team member involvement activities.
Performs other duties as assigned.