Senior Machine Learning Operations MLOps Engineer

Symphony Solutions

Cincinnati (OH)

Hybrid

USD 140,000 - 210,000

Full time

4 days ago

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Benefits offered by this job

Competitive salary

Flexible schedule

Training & development

Flexible work from home options

Job summary

Symphony Solutions seeks a Senior MLOps Engineer to design, build, and optimize scalable ML platforms and production ML systems in cloud environments. You will lead CI/CD for ML workloads and collaborate with data scientists, data engineers, software engineers, and business stakeholders.

Responsibilities include deploying end-to-end ML pipelines, feature stores, real-time scoring, monitoring, governance, and cost-efficient operations. Flexible work-from-home options are available.

Qualifications

Extensive experience managing the ML lifecycle from data prep to deployment.
Proven ability to deploy and orchestrate ML models in production.
Experience tracking experiments and versioning models.
Strong monitoring/observability for ML systems.
Knowledge of feature stores and model governance.

Responsibilities

Design, develop, deploy, and manage production-grade ML solutions.
Build scalable ML platforms supporting training, validation, deployment, and monitoring.
Develop frameworks for batch and real-time inference workloads.
Optimize ML systems for performance, reliability, and cost.

Skills

ML lifecycle management

Model deployment & orchestration

Experiment tracking & versioning

Model monitoring & observability

Feature store architecture

Tools

Snowflake

Databricks

IBM DB2

Job description

Benefits:

Competitive salary
Flexible schedule
Training & development

We are seeking a Senior Machine Learning Operations (MLOps) Engineer to design, build, and optimize scalable machine learning platforms and production ML systems.

Candidate must have 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.

Key Responsibilities

ML Platform Engineering & Model Operations

Design, develop, deploy, and manage production-grade machine learning solutions.
Build scalable and reusable machine learning platforms that support model development, training, validation, deployment, and monitoring.
Develop frameworks to support both batch and real-time inference workloads.
Optimize machine learning systems for performance, reliability, maintainability, and cost efficiency.

MLOps Pipeline Development

Design and maintain automated CI/CD/CT (Continuous Integration, Continuous Delivery, Continuous Training) pipelines for machine learning workflows.
Automate model testing, validation, deployment, rollback, and retraining processes.

Feature Store & Data Engineering Integration

Design and support enterprise feature stores to improve feature reuse, consistency, and model governance.
Collaborate with data engineering teams to integrate machine learning solutions with data warehouses, data lakes, and operational data sources.

Production Monitoring & Reliability

Establish alerting, observability, and incident management processes.
Develop strategies for model retraining and continuous performance improvement.

Real-Time Machine Learning Systems

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 capabilities.

Governance, Security & Compliance

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.

Required Technical Skills

Machine Learning & MLOps