{"id":3710405,"url":"https://alion.io/job/tyler-technologies-mlai-data-and-cloud-engineer","title":"ML/AI Data and Cloud Engineer","company":{"id":179690,"name":"Tyler Technologies","domain":"tyler.com","url":"https://alion.io/company/tyler-technologies","size_band":"501-1000","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Jobvite","truth_index":{"grade":"A","score":100,"open_postings":4,"ghost_share":0,"stale_share":0,"repost_share":0,"time_to_fill_p50_days":16,"computed_at":"2026-11-06T05:58:05Z"}},"role":"AI/ML","role_family":"AI/ML","seniority":"middle","employment_type":null,"work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":[],"countries":[],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":null,"experience_years_min":3,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"AWS","optional":false},{"name":"Machine Learning","optional":false},{"name":"Platform Engineering","optional":false},{"name":"Python","optional":false},{"name":"AI Agents","optional":true},{"name":"Amazon EC2","optional":true},{"name":"Amazon ECS","optional":true},{"name":"Amazon S3","optional":true},{"name":"Amazon SageMaker","optional":true},{"name":"AWS Lambda","optional":true},{"name":"Docker","optional":true},{"name":"LLM","optional":true},{"name":"Podman","optional":true}],"status":"live","first_seen_at":"2026-11-03T22:53:23Z","employer_posted_date":"2026-11-03","last_verified_at":"2026-11-06T05:08:22Z","board_verified":true,"closed_at":null,"days_open":2,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":2},"description":"Description\nWe 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.\nThis 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.\nThe 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.\nResponsibilities\nDesign and implement cloud-native data platforms that integrate structured and unstructured data into scalable data lakes or unified data systems\nBuild and maintain robust ingestion pipelines using API-driven, streaming, and batch processing workflows\nArchitect data processing systems for feature engineering, large-scale computation, and model training support\nDevelop and operate model-serving infrastructure enabling real-time and batch inference via APIs\nDevelop advanced modeling approaches combining statistical methods and machine learning (e.g., regression, gradient boosting, random forests, and ensemble/meta-learning techniques)\nImplement model validation and monitoring frameworks addressing bias, drift, robustness, and performance\nTune cloud infrastructure for performance, reliability, and cost efficiency across compute, storage, and data transfer layers\nBuild and maintain MLOps pipelines for model deployment, versioning, monitoring, and automated retraining\nEnsure platform reliability, fault tolerance, and data integrity across all stages of the data and model lifecycle\nQualifications\nBachelor’s degree in Computer Science, Data Engineering, Engineering, Applied Mathematics, or related field (or equivalent experience)\n3+ years of experience in data engineering, cloud engineering, ML platform engineering, machine learning engineering, AI systems, quantitative modeling, or data-intensive platform development\nStrong programming skills in Python and experience with backend, data engineering, or experience with modern ML frameworks and data tooling\nExperience designing and operating cloud-native systems (AWS or equivalent)\nExperience building and maintaining large-scale data pipelines and working with high-volume datasets\nExperience developing APIs or microservices for production systems\nSolid understanding of distributed systems, data processing, and system reliability concepts\nSolid foundation in algorithms, data structures, and computational thinking\nPreferred Qualifications\nFamiliarity with modern AI-assisted development tools and workflows (e.g., LLM-based coding assistants, agentic development environments, or similar tools)\nExposure to machine learning workflows and the infrastructure required to support training and inference at scale\nExperience with cloud services (e.g., AWS: S3, EC2, Lambda, Glue, SageMaker, or equivalents)\nExperience with containerization (Docker/Podman, ECS)\nExperience with data lake architectures and multi-source data ingestion patterns\nFamiliarity with MLOps practices, including reproducibility, model versioning, and deployment automation\nWorking knowledge of statistics, econometrics, quantitative modeling, and optimization\nFamiliarity with cloud cost optimization strategies and performance tuning \nWhat You’ll Work On\nScalable cloud data platforms integrating diverse, high-volume datasets\nFoundational predictive models that adapt across markets rather than being rebuilt for each use case\nA cloud-native platform supporting data ingestion, model training, inference, and scalable deployment\nIntelligent systems for ranking, matching, scoring, and explanation of model outputs\nRobust evaluation and monitoring frameworks for model performance and reliability\nAPI-driven integration of AI capabilities into broader software ecosystems\nWhy Join Us?\nYou’ll work on challenging, high-scale systems at the intersection of data engineering, cloud infrastructure, and machine learning. 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