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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Principal Scientist, Data - **Company:** Objectwin Technology, Inc. - **Location:** Houston, TX, United States - **Experience:** Expert - **Contract:** Permanent contract - **Skills:** A/B Testing, Application Programming Interfaces (APIs), Artificial Intelligence, Airflow, Amazon Web Services, ARM Architecture, Microsoft Azure, Cloud Computing, Cloud Engineering, Computer Programming, Computer Engineering, Continuous Delivery, Continuous Integration, Information Engineering, DevOps, Digital Architecture, Distributed Computing Environment, Monitoring of Systems, Python (Programming Language), Machine Learning, Operational Databases, OpenAI, Azure Machine Learning, SQL Databases, Systems Integration, Data Logging, Feature Store, Cloud Platform System, Feature Engineering, Data Ingestion, Azure Data Factory, Retrieval-Augmented Generation, Delivery Pipeline, Large Language Models, Snowflake, Software Troubleshooting, AWS Lambda, Cloudformation, Containerization, Infrastructure Automation Frameworks, Real-time Inference, Information Technology, Deployment Automation, Star Schema, Data Management, Machine Learning Operations, Software Coding, Terraform, Software Version Control, Data Pipelines, Serverless Computing, Docker - **Published:** October 27, 2026 - **Apply:** https://www.careerbuilder.com/job-details/principal-scientist-data-houston-tx--f9cccbb7-5cfa-4e6e-b9e3-1731c4d0e36c ## About the Role Machine Learning, Data Science, Microsoft Windows Azure, Equipment Maintenance/Repair, Snowflake Schema, Cloud Computing, Amazon Web Services (AWS), Data Management, Automation, Continuous Deployment/Delivery, The ideal candidate has strong experience in the end-to-end ML lifecycle, cloud-native deployment, CI/CD automation, model monitoring, and production data pipelines, with hands-on expertise in AWS, Azure, and Snowflake., Master's or Advanced degree (PhD) in Computer Science, Computer Engineering, or Similar Five or more years of relevant experiences Proven experience in MLOps, ML engineering, platform engineering, or DevOps Strong hands-on experience with AWS, Microsoft Azure, and Snowflake Strong programming skills in Python and SQL Experience deploying and managing ML models in production Experience with cloud ML services such as AWS SageMaker and Azure Machine Learning Experience building data pipelines and integrating with Snowflake Knowledge of CI/CD pipelines, infrastructure automation, and model versioning Experience with containerization and orchestration tools such as Docker and Kubernetes Experience with workflow orchestration tools such as Airflow, Azure Data Factory, or similar Familiarity with model monitoring, logging, alerting, and observability Solid understanding of data engineering concepts, APIs, and distributed processing Strong troubleshooting, communication, and cross-team collaboration skills Preferred Qualifications Experience with Snowflake Cortex AI, Snowpark, or ML workloads in Snowflake Experience with AWS Bedrock, Azure OpenAI, or production LLM workflows Experience with real-time inference, event-driven pipelines, and serverless architectures Familiarity with feature stores, vector databases, and RAG-based systems Experience with Terraform, CloudFormation, or Azure infrastructure-as-code tools Understanding of security, compliance, and governance requirements for regulated environments Experience with production A/B testing, shadow deployment, and rollback strategies ## Description Must-have:Hands-on experience with AWS, Microsoft Azure, and Snowflake in building or supporting production ML/data platforms., We are seeking an MLOps Engineer to design, deploy, monitor, and maintain machine learning solutions in production across AWS, Microsoft Azure, and Snowflake environments. This role will partner with data scientists and cloud teams to operationalize ML models, automate pipelines, and build reliable, secure, and scalable ML platforms., Design and implement end-to-end ML pipelines for data ingestion, feature engineering, model training, validation, deployment, and monitoring Deploy and manage ML models in production across AWS, Azure, and Snowflake-based ecosystems Build batch and real-time inference pipelines using cloud-native and platform-native services Automate model packaging, testing, release, and rollback using CI/CD best practices Integrate ML workflows with services such as AWS SageMaker, AWS Lambda, Azure Machine Learning, Azure Data Factory, and Snowflake Build and maintain orchestration workflows using tools such as Airflow, Azure Data Factory, or similar platforms Implement experiment tracking, model registry, and model governance processes Monitor model accuracy, drift, latency, throughput, pipeline failures, and infrastructure usage Establish deployment strategies such as canary, shadow, blue-green, and rollback mechanisms Collaborate with cross-functional teams to move models from research to production Ensure security, compliance, traceability, and access control for models and data across cloud environments Optimize platform performance, reliability, and cost across AWS, Azure, and Snowflake Document architecture, deployment standards, and operational procedures