Senior Machine Learning Operations (MLOps) Engineer

Eliassen Group; Cincinnati (OH); On-site; USD 103,000 - 117,000; Full time

Benefits offered by this job

Medical benefits

Dental benefits

Vision benefits

401k with company matching

Life insurance

Job summary

Eliassen Group in Cincinnati, OH is seeking a Senior Machine Learning Operations (MLOps) Engineer to design, build, and optimize scalable ML platforms across the end-to-end lifecycle. You will enable feature engineering, model development, automated deployment, monitoring, governance, and continuous improvement in cloud environments while partnering with data scientists, engineers, and business stakeholders.

On-site W-2 role with comprehensive benefits.

Qualifications

• 5+ years in Machine Learning Engineering, MLOps, or Data/Software Eng.
• 3+ years building production ML systems.
• End-to-end ML lifecycle management experience.
• Cloud experience with AWS, Azure, or GCP.

Responsibilities

• Design, develop, deploy, and manage production-grade ML solutions.
• Build scalable ML platforms for development, training, validation, deployment, and monitoring.
• Implement model versioning, experimentation tracking, and governance.
• Develop batch and real-time inference frameworks.
• Automate CI/CD pipelines for ML workflows.
• Automate model testing, deployment, rollback, and retraining.

Skills

ML Ops

Cloud platforms

CI/CD

Terraform / IaC

Python

SQL

Monitoring

Data governance

Tools

Python

SQL

Docker

Kubernetes

Snowflake

Databricks

dbt

Spark

Job description

Description:

On-site in Cincinnati, OH

Our client seeks a Senior Machine Learning Operations (MLOps) Engineer to design, build, and optimize scalable platforms and production ML systems across the end-to-end lifecycle. You will enable feature engineering, model development, automated deployment, monitoring, governance, and continuous improvement in cloud environments while partnering with data scientists, data engineers, software engineers, and business stakeholders. Experience in regulated industries is desirable.

Due to client requirements, applicants must be willing and able to work on a w2 basis. For our w2 consultants, we offer a great benefits package that includes Medical, Dental, and Vision benefits, 401k with company matching, and life insurance.

Rate: $75.00 to $85.00/hr. w2

Responsibilities:

• Design, develop, deploy, and manage production-grade machine learning solutions.
• Build scalable and reusable ML platforms for development, training, validation, deployment, and monitoring.
• Implement best practices for model versioning, experimentation tracking, governance, and reproducibility.
• Develop frameworks for batch and real-time inference.
• Optimize ML systems for performance, reliability, maintainability, and cost.
• Design and maintain automated CI/CD/CT pipelines for ML workflows.
• Automate model testing, validation, deployment, rollback, and retraining.
• Establish infrastructure-as-code and automated environment provisioning.
• Build operational tooling for model lifecycle management across environments.
• Design and support enterprise feature stores to improve reuse and governance.
• Integrate ML solutions with data warehouses, data lakes, and operational sources.
• Develop scalable data pipelines for training and inference.
• Optimize data availability, quality, lineage, and observability.
• Implement monitoring for model performance, data quality, and drift.
• Establish alerting, observability, and incident management processes.
• Develop strategies for retraining and continuous improvement.
• Build and support low-latency scoring and prediction platforms.
• Develop event-driven ML architectures using streaming technologies.
• Implement MLOps governance standards and controls with security and compliance alignment.
• Support auditability, explainability, lineage tracking, and documentation.
• Collaborate with cross-functional teams and mentor junior engineers.
• Drive adoption of modern ML engineering methodologies.

Experience Requirements:

• 5+ years in Machine Learning Engineering, MLOps, Data Engineering, or Software Engineering.
• 3+ years building and supporting production ML systems.
• End-to-end ML lifecycle management experience.
• Cloud experience with AWS, Azure, or GCP.
• Experience with batch and real-time ML solutions and large-scale data.
• ML and MLOps: lifecycle management, deployment and orchestration, experiment tracking and versioning, monitoring and observability, feature store and model registry, automated retraining, responsible AI and governance.
• Cloud platforms: AWS SageMaker, Azure ML, or Google Vertex AI.
• Programming: Python and SQL required; Java, Scala, or R preferred.
• Data platforms: Snowflake, Databricks, IBM DB2 or equivalent, data lakes and cloud storage.
• Data transformation: dbt, Spark, Pandas, PySpark.
• DevOps and infrastructure: Git-based workflows, CI/CD tools, Terraform or IaC, Linux, shell scripting.
• Containers and orchestration: Docker, Kubernetes, OpenShift preferred.
• Streaming and real-time: Apache Kafka, event-driven architectures, real-time inference pipelines.
• Monitoring and observability: Prometheus, Grafana, ELK, cloud-native solutions.
• Preferred: enterprise feature stores, model registry and governance frameworks, real-time decisioning platforms, credit risk or fraud or customer analytics or recommender systems, MLFlow or Kubeflow or Airflow or Argo or Feast or SageMaker Pipelines, distributed systems, AI/ML regulatory frameworks, relevant cloud certifications.