Seeking a Senior Machine Learning Operations (MLOps) Engineer for hybrid contract opportunity with my direct client in Cincinnati OH.
If interested , share your word document resume, work authorization, expected hourly pay rate, current location, availability for hybrid work , skill stack and LinkedIn.
Senior Machine Learning Operations (MLOps) Engineer
Position Summary
We are seeking a Senior Machine Learning Operations (MLOps) Engineer to design, build, and optimize scalable machine learning platforms and production ML systems. This role will focus on enabling end-to-end machine learning lifecycle management, including feature engineering, model development, deployment automation, monitoring, governance, and continuous improvement.
The ideal candidate has 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. Experience working in highly regulated industries such as financial services, banking, healthcare, insurance, or telecommunications is highly desirable.
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.
Implement best practices for model versioning, experimentation tracking, governance, and reproducibility.
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.
Establish infrastructure-as-code practices and automated environment provisioning.
Build operational tooling to support model lifecycle management across development, testing, and production environments.
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.
Develop scalable data pipelines supporting both training and inference workloads.
Optimize data availability, quality, lineage, and observability.
Production Monitoring & Reliability
Implement model monitoring frameworks covering:
Model performance
Data quality
Data drift
Concept drift
Feature drift
System health and 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.
Ensure compliance with enterprise security, privacy, risk, and regulatory requirements.
Support model auditability, explainability, lineage tracking, and documentation.
Partner with risk, compliance, and cybersecurity teams to maintain operational standards.
Collaboration & Technical Leadership
Collaborate closely with data scientists, analytics teams, product owners, and software engineers.
Translate business requirements into scalable technical solutions.
Mentor junior engineers and contribute to MLOps standards, patterns, and best practices.
Drive adoption of modern machine learning engineering methodologies across the organization.
Required Qualifications
Experience
5+ years of experience in Machine Learning Engineering, MLOps, Data Engineering, or Software Engineering.
3+ years of experience building and supporting production machine learning systems.
Demonstrated experience implementing end-to-end machine learning lifecycle management.
Experience operating ML workloads in cloud environments (AWS, Azure, or Google Cloud Platform).
Experience supporting both batch and real-time machine learning solutions.
Experience working with large-scale structured and unstructured data.
Required Technical Skills
Machine Learning & MLOps
ML lifecycle management
Model deployment and orchestration
Experiment tracking and model versioning
Model monitoring and observability
Feature Store architecture
Model Registry implementation
Automated retraining workflows
Responsible AI and model governance practices
Cloud Platforms
Experience with one or more:
AWS SageMaker
Azure Machine Learning
Google Vertex AI
Programming
Proficiency in:
Python (required)
SQL (required)
Java, Scala, or R (preferred)
Data Platforms
Experience with:
Snowflake
Databricks
IBM DB2 or equivalent enterprise RDBMS platforms
Data lakes and modern cloud storage platforms
Data Transformation
Experience with:
dbt
Spark
Pandas
PySpark
DevOps & Infrastructure
Experience with:
Git/GitHub/GitLab
Jenkins, GitHub Actions, Azure DevOps, or equivalent CI/CD tools
Terraform or infrastructure-as-code frameworks
Linux administration
Shell scripting
Containers & Orchestration
Experience with:
Docker
Kubernetes
OpenShift (preferred)
Streaming & Real-Time Technologies
Experience with:
Apache Kafka
Event-driven architectures
Real-time inference pipelines
Monitoring & Observability
Experience with:
Prometheus
Grafana
ELK Stack
Cloud-native monitoring solutions
Experience implementing enterprise Feature Stores.
Experience designing Model Registry and ML Governance frameworks.
Experience building real-time decisioning or scoring platforms.
Experience supporting credit risk, fraud, customer analytics, recommendation systems, or predictive analytics use cases.
Experience with MLFlow, Kubeflow, Airflow, Argo Workflows, Feast, SageMaker Pipelines, or similar MLOps platforms.
Knowledge of software architecture patterns and distributed systems.
Familiarity with AI/ML regulatory frameworks and model risk management practices.
Relevant cloud certifications (AWS, Azure, Google Cloud Platform) preferred.
Desired Outcomes
The engineer will help establish and mature enterprise MLOps capabilities, including:
Feature Store implementation and adoption.
Model Registry and governance processes.
Automated CI/CD/CT pipelines.
Real-time model deployment and scoring.
Enterprise machine learning monitoring and observability.
Reusable machine learning platform services that accelerate model development and deployment across data science teams.
