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Open nowPosted yesterday

ML/AI Data and Cloud Engineer

Tyler Technologies423 open roles

Where
Moraine, OH, United States
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Your applicationOpen nowML/AI Data and Cloud EngineerTyler Technologies · Moraine, OH, United States

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Share of postings closed within
  1. 2.1%1 day
  2. 4.6%3 days
  3. 13.3%7 days
  4. 21.3%14 days
  5. 35.7%30 days
  6. 59.1%60 days
This job: posted yesterday

The posting

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.

Responsibilities

  • 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

Qualifications

  • 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

Preferred Qualifications

  • 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, SageMaker, 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

What You’ll Work On

  • 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 data ingestion, model training, inference, and scalable deployment
  • Intelligent systems for ranking, matching, scoring, and explanation of model outputs
  • Robust evaluation and monitoring frameworks for model performance and reliability
  • API-driven integration of AI capabilities into broader software ecosystems

Why Join Us?

You’ll work on challenging, high-scale systems at the intersection of data engineering, cloud infrastructure, and machine learning. This role offers the opportunity to build foundational platforms that support advanced AI capabilities while shaping architecture, tooling, and best practices in a rapidly evolving space.

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