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Machine Learning Engineer III

Javen Technologies

  • Cincinnati, OH
  • 4 days ago

    Highlights

    MLOps Pipeline Development Design and maintain automated CI/CD/CT (Continuous Integration, Continuous Delivery, Continuous Training) pipelines for machine learning workflows. Feature Store & Data Engineering Integration Design and support enterprise feature stores to improve feature reuse, consistency, and model governance.

    Numbers & Facts

    LocationCincinnati, OH

    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.
     

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