ML​/AI Data and Cloud Engineer

We are seeking an ML/AI Data & Cloud Engineer to design, build, and operate the cloud-native components that deliver large-scale, data-intensive AI capabilities.

This role focuses on building scalable data platforms, distributed processing systems, and model-serving infrastructure that enable end-to-end machine learning workflows in production. You will work closely with data scientists and product teams to transform advanced models into reliable, high-performance services.

The ideal candidate combines strong engineering skills with a solid grounding in quantitative methods, and is comfortable leveraging modern AI-assisted development tools to accelerate research, prototyping, and delivery.

Remote | Moraine, Ohio

• Design and implement cloud-native data platforms that integrate structured and unstructured data into scalable data lakes or unified data systems
• Build and maintain robust ingestion pipelines using API-driven, streaming, and batch processing workflows
• Architect data processing systems for feature engineering, large-scale computation, and model training support
• Develop and operate model-serving infrastructure enabling real-time and batch inference via APIs
• Develop advanced modeling approaches combining statistical methods and machine learning (e.g., regression, gradient boosting, random forests, and ensemble/meta-learning techniques)
• Implement model validation and monitoring frameworks addressing bias, drift, robustness, and performance
• Tune cloud infrastructure for performance, reliability, and cost efficiency across compute, storage, and data transfer layers
• Build and maintain MLOps pipelines for model deployment, versioning, monitoring, and automated retraining
• Ensure platform reliability, fault tolerance, and data integrity across all stages of the data and model lifecycle

• Bachelor's degree in Computer Science, Data Engineering, Engineering, Applied Mathematics, or related field (or equivalent experience)
• 3+ years of experience in data engineering, cloud engineering, ML platform engineering, machine learning engineering, AI systems, quantitative modeling, or data-intensive platform development
• Strong programming skills in Python and experience with backend, data engineering, or experience with modern ML frameworks and data tooling
• Experience designing and operating cloud-native systems (AWS or equivalent)
• Experience building and maintaining large-scale data pipelines and working with high-volume datasets
• Experience developing APIs or microservices for production systems
• Solid understanding of distributed systems, data processing, and system reliability concepts
• Solid foundation in algorithms, data structures, and computational thinking

• Familiarity with modern AI-assisted development tools and workflows (e.g., LLM-based coding assistants, agentic development environments, or similar tools)
• Exposure to machine learning workflows and the infrastructure required to support training and inference at scale
• Experience with cloud services (e.g., AWS: S3, EC2, Lambda, Glue, Sage Maker, or equivalents)
• Experience with containerization (Docker/Podman, ECS)
• Experience with data lake architectures and multi-source data ingestion patterns
• Familiarity with MLOps practices, including reproducibility, model versioning, and deployment automation
• Working knowledge of statistics, econometrics, quantitative modeling, and optimization
• Familiarity with cloud cost optimization strategies and performance tuning

• Scalable cloud data platforms integrating diverse, high-volume datasets
• Foundational predictive models that adapt across markets rather than being rebuilt for each use case
• A cloud-native platform supporting…