Senior Machine Learning Operations (MLOps) Engineer
Location: Cincinnati, OH
Need candidate to work onsite from Day 1 (3-4 days a week)
Highly Preferred Local Candidates
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.
Tekshapers is an equal opportunity employer and will consider all applications without regards to race, sex, age, color, religion, national origin, veteran status, disability, sexual orientation, gender identity, genetic information or any characteristic protected by law.
