Data Scientist

Title: ML Ops Engineer Location: Houston, TX 77002 Duration: 8 months Pay Range: $70/hr - $78/hr The Company offers the following benefits for this position, subject to applicable eligibility requirements: medical insurance, dental insurance, vision insurance, 401(k) retirement plan, life insurance, long-term disability insurance, short-term disability insurance, paid parking/public transportation, (paid time , paid sick and safe time , hours of paid vacation time, weeks of paid parental leave, paid holidays annually - AS Applicable) Must-have: Strong MLOps experience, Hands-on experience with AWS, MS Azure, and Snowflake in building or supporting production Machine Learning /data platforms. Job Summary We are seeking an MLOps Engineer to design, deploy, monitor, and maintain machine learning solutions in production across AWS, MS Azure, and Snowflake environments. This role will partner with data scientists and cloud teams to operationalize Machine Learning models, automate pipelines, and build reliable, secure, and scalable Machine Learning  platforms. The ideal candidate has strong experience in the end-to-end Machine Learning lifecycle, cloud-native deployment, CI/CD automation, model monitoring, and production data pipelines, with hands-on expertise in AWS, Azure, and Snowflake. Key Responsibilities • Design and implement end-to-end Machine Learning pipelines for data ingestion, feature engineering, model training, validation, deployment, and monitoring • Deploy and manage Machine Learning 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 Machine Learning 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 Required Qualifications • Master’s or Advanced degree (PhD) in Computer Science, Computer Engineering, or Similar • Five or more years of relevant experiences • Proven experience in MLOps, Machine Learning engineering, platform engineering, or DevOps • Strong hands-on experience with AWS, MS Azure, and Snowflake • Strong programming skills in Python and SQL • Experience deploying and managing Machine Learning models in production • Experience with cloud Machine Learning 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 Machine Learning 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