Senior Machine Learning Operations MLOps Engineer

Symphony Solutions•Cincinnati, OH

1d•$60 - $75

About The Position

We are seeking a Senior Machine Learning Operations (MLOps) Engineer to design, build, and optimize scalable machine learning platforms and production ML systems. The role calls for strong experience building and operationalizing machine learning solutions in cloud environments, developing CI/CD pipelines for ML workloads, and collaborating with data scientists, data engineers, software engineers, and business stakeholders. The engineer will develop production-grade ML platforms and workflows covering model development, training, validation, deployment, monitoring, and continuous improvement. The role also supports batch and real-time inference, feature store integration, event-driven architectures, model governance, security, and compliance. Flexible work-from-home options are available.

Requirements

Strong experience building and operationalizing machine learning solutions in cloud environments.
Strong experience developing CI/CD pipelines for ML workloads.
Experience with ML lifecycle management, model deployment and orchestration, experiment tracking and model versioning, model monitoring and observability, and feature store architecture.
Experience with one or more of AWS SageMaker, Azure Machine Learning, or Google Vertex AI.
Proficiency in Python and SQL.
Experience with Snowflake, Databricks, IBM DB2 or equivalent enterprise RDBMS platforms, data lakes, and modern cloud storage platforms.
Experience with dbt, Spark, Pandas, or PySpark.
Experience with Git, GitHub, or GitLab; Jenkins, GitHub Actions, Azure DevOps, or equivalent CI/CD tools; Terraform or other infrastructure-as-code frameworks; Linux administration; and shell scripting.
Experience with Docker and Kubernetes.
Experience with Apache Kafka, event-driven architectures, and real-time inference pipelines.
Experience with Prometheus, Grafana, ELK Stack, or cloud-native monitoring solutions.
Ability to collaborate with data scientists, data engineers, software engineers, business stakeholders, and partner teams.

Nice To Haves

Proficiency in Java, Scala, or R.
Experience with OpenShift.

Responsibilities

Design, develop, deploy, and manage production-grade machine learning solutions.
Build scalable, reusable machine learning platforms supporting model development, training, validation, deployment, and monitoring.
Develop frameworks for batch and real-time inference workloads.
Optimize machine learning systems for performance, reliability, maintainability, and cost efficiency.
Design and maintain automated CI/CD/CT pipelines for machine learning workflows.
Automate model testing, validation, deployment, rollback, and retraining processes.
Design and support enterprise feature stores to improve feature reuse, consistency, and model governance.
Collaborate with data engineering teams to integrate ML solutions with data warehouses, data lakes, and operational data sources.
Establish alerting, observability, and incident management processes.
Develop model retraining and continuous performance improvement strategies.
Build and support low-latency scoring and prediction platforms.
Develop event-driven machine learning architectures using streaming technologies.
Partner with business and engineering teams to enable near real-time decisioning.
Implement MLOps governance standards and controls.
Support model auditability, explainability, lineage tracking, and documentation.
Partner with risk, compliance, and cybersecurity teams to maintain operational standards.