Senior Machine Learning Engineer

Senior Machine Learning Operations (MLOps) Engineer

Cincinnati, OH – Need Local

Contract

Direct Client

Skills:

MLOps / ML Engineering

Python & SQL

Cloud ML Platforms

CI/CD & ML Pipelines

Model Deployment & Monitoring

MLflow / Kubeflow

Docker & Kubernetes

Databricks, Snowflake, Spark, PySpark, dbt      

Terraform, Git, Linux, Shell Scripting

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

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, 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.