JT
Machine Learning Engineer III
Javen Technologies
- 🇺🇸 United States
- On-site
- 17 hours ago
- Machine Learning
- MLOps
- Square
- CI/CD
- IaC
- Incident Management
- AWS
- Azure
- GCP
- AI
- AWS SageMaker
- Vertex AI
- Python
- SQL
- Java
- Scala
- Snowflake
- Databricks
- DB2
- dbt
- Pandas
- PySpark
- Devops
- Git
- GitHub
- GitLab
- Jenkins
- GitHub Actions
- Azure DevOps
- Terraform
- Linux
- Docker
- Kubernetes
- OpenShift
- Apache Kafka
- Prometheus
- Grafana
- Elastic Stack
- MLflow
- Kubeflow
- Airflow
- Argo Workflows
- AI/ML
- Risk Management
17 hours ago
Location: 38 Fountain Square Plaza, Cincinnati, OH – 45202
Duration: 12 Months contract
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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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
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.
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
Experience with one or more:
- AWS SageMaker
- Azure Machine Learning
- Google Vertex AI
Proficiency in:
- Python (required)
- SQL (required)
- Java, Scala, or R (preferred)
Experience with:
- Snowflake
- Databricks
- IBM DB2 or equivalent enterprise RDBMS platforms
- Data lakes and modern cloud storage platforms
Experience with:
- dbt
- Spark
- Pandas
- PySpark
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
Experience with:
- Docker
- Kubernetes
- OpenShift (preferred)
Experience with:
- Apache Kafka
- Event-driven architectures
- Real-time inference pipelines
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.
Machine Learning Engineer III · Javen Technologies