Senior Machine Learning Operations (MLOps) Engineer

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.