ML Ops Architect

over 1 year ago

Remote, United States or Dallas, TX, USASenior

Responsibilities

Implement scalable cloud architectures and systems for machine learning model inference at scale.
Deploy, manage, monitor, and maintain machine learning and data pipelines in production.
Build containerization and orchestration solutions for model deployment.
Develop MLOps practices covering CI/CD pipelines, version control, model versioning, monitoring, alerting, and automated deployment.
Manage ML infrastructure and cloud compute resources for high availability, performance, and scalability.
Implement monitoring, logging, and performance analysis for deployed models and systems.
Troubleshoot production issues involving model deployment, performance, and scalability.
Apply security, compliance, data privacy, responsible AI, and explainable AI practices to ML systems.
Develop documentation, standard operating procedures, and guidelines for MLOps processes and tools.

Master's or doctoral degree in computer science, electrical engineering, mathematics, or a similar field.
Typically 7+ years of hands-on experience developing and applying advanced analytics solutions in a corporate environment.
At least 4 years of programming experience with Python.
At least 3 years of experience designing and building data-intensive solutions using distributed computing.
At least 3 years of experience productionizing, monitoring, and maintaining machine learning models.
Experience with Azure Machine Learning, Azure Data Factory, Azure Databricks, Azure Kubernetes Service, and Azure Monitor.
Experience deploying AI and machine learning solutions at scale using AWS, Azure, or Google Cloud Platform.
Experience developing and maintaining APIs and specifying infrastructure as code using tools such as Ansible and Terraform.
Experience designing, developing, and scaling data and feature pipelines for ML models.
Ability to work across programming languages and MLOps technologies including Python, Spark, Databricks, GitHub, MLflow, and Airflow.
Expertise in Unix Shell scripting and dependency-driven job schedulers.
Experience with ML infrastructure security, compliance, data privacy, and visualization technologies such as RShiny, Streamlit, Python Dash, Tableau, and Power BI.

Fully remote, full-time position based in Dallas, United States.
Significant career development opportunities in a small, fast-growing, challenging, and entrepreneurial environment.
High degree of individual responsibility and opportunity to work on advanced analytics, machine learning, and AI platforms.

5,001-10,000 employees