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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Emerging Technology Solutions Architect - Machine Learning - **Company:** U.S. Bank - **Location:** Cupertino, CA, United States - **Experience:** Expert - **Salary:** $139,230.0 - $163,800.0 - **Contract:** Permanent contract - **Skills:** Amazon Web Services, Information Engineering, Machine Learning, Open Source Technology, Recommender Systems, Tensorflow, Azure Machine Learning, Software Deployment, Software Engineering, Supervised Learning, Cloud Platform System, Feature Engineering, Data Ingestion, Pytorch, Large Language Models, Snowflake, Model Validation, Generative AI, Scikit Learn, Kubernetes, Xgboost, Machine Learning Operations, Software Version Control, Unsupervised Learning, Databricks - **Published:** August 25, 2026 - **Apply:** https://dejobs.org/x/x/CE083A29ADC54908BCCC37F81426F1A1/job/ ## About the Role * Bachelor's degree or equivalent work experience. * Eight (8) or more years of experience in software engineering, machine learning engineering, data engineering, solution architecture, or related technical roles. Preferred Skills / Experience * Strong foundation in machine learning, software engineering, and solution architecture . * Experience designing and deploying production machine learning systems in cloud environments. * Expertise in MLOps practices , including CI/CD pipelines, model versioning, monitoring, governance, and automated retraining. * Hands-on experience with machine learning platforms such as Azure Machine Learning, AWS SageMaker, Databricks, Snowflake ML, MLflow, or Kubeflow . * Knowledge of machine learning frameworks including PyTorch, TensorFlow, Scikit-learn, XGBoost, or similar technologies . * Experience architecting solutions involving feature stores, model serving, real-time inference, batch scoring, and machine learning pipelines . * Understanding of machine learning concepts including supervised learning, unsupervised learning, forecasting, recommendation systems, anomaly detection, and model explainability . * Experience making architecture decisions grounded in real-world tradeoffs including cost, performance, scalability, security, governance, and model accuracy . * Ability to design solutions and provide technical guidance through implementation, not purely conceptual architecture. * Strong communication, stakeholder alignment, and cross-functional leadership skills. * Familiarity with generative AI and large language models is preferred but not required. ## Description * Evaluate emerging machine learning technologies, platforms, frameworks, and tooling ecosystems for enterprise adoption. * Assess ML technologies and services including Azure Machine Learning, AWS SageMaker, Databricks, Snowflake ML, and open-source ML frameworks . * Define scalable architectures supporting the end-to-end machine learning lifecycle, including data ingestion, feature engineering, model training, deployment, monitoring, and governance . * Recommend architecture patterns based on performance, scalability, security, explainability, and operational risk requirements. * Establish reusable solution patterns for MLOps, model serving, feature stores, automated retraining, model monitoring, and observability . * Design and recommend production-ready machine learning solutions with sufficient technical depth to support engineering and data science teams through implementation. * Evaluate vendor platforms and ecosystem offerings for enterprise fit, long-term viability, and business value. * Partner with data scientists and engineering teams to operationalize machine learning models at scale. * Provide technical leadership on machine learning architecture, MLOps, model lifecycle management, and production deployment strategies. * Establish standards and best practices for model governance, observability, explainability, and responsible AI. * Translate complex technical concepts into clear recommendations for technical and non-technical stakeholders. * Assess emerging machine learning technologies and translate exploratory findings into enterprise-ready recommendations. ## Related Videos - [TikTok's Privacy Innovation](https://www.wearedevelopers.com/videos/1036-tiktok-s-privacy-innovation) - [Photonic Computing: Programming a New Class of AI Accelerators (incl. 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