NEW JOB OPENING
MLOPS ENGINEER
IN Houston, TX, USA!

Education:

• Master’s or Advanced degree (PhD) in Computer Science, Computer Engineering, or Similar



Required Qualifications/Skills/Experience:

• Strong MLOps experience, Hands-on experience with AWS, Microsoft Azure, and Snowflake in building or supporting production ML/data platforms.

• 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 Sage Maker 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, Snow Park, or ML workloads in Snowflake

• Experience with AWS Bedrock, Azure Open AI, or production LLM workflows

• Experience with real-time inference, event-driven pipelines, and server less architectures

• Familiarity with feature stores, vector databases, and RAG-based systems

• Experience with Terraform, Cloud Formation, 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



Job Summary:

• We are seeking a ML Ops 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.

• 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.



Key Responsibilities:

• 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

• **Only those lawfully authorized to work in the designated country associated with the position will be considered.**

• **Please note that all Position start dates and duration are estimates and may be reduced or lengthened based upon a client’s business needs and requirements.**