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Machine Learning Engineer III

Javen Technologies

Cincinnati, OH 45202 • 10/28/2026

Engineering , Professional , Skilled & Trades , Manufacturing

Job Description

Job Description
Title: Senior Machine Learning Operations (MLOps) Engineer
Location: 38 Fountain Square Plaza, Cincinnati, OH – 45202
Duration: 12 Months contract

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 GCP).
  • 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

Preferred Qualifications
  • 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, GCP) 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.