Data Scientist / Machine Learning Engineer

VSG Business Solutions•Cincinnati, OH

15h

About The Position

We are seeking a Senior Machine Learning Operations (MLOps) Engineer to design, build, and optimize scalable machine learning platforms and production ML systems. The role enables end-to-end machine learning lifecycle management, including feature engineering, model development, deployment automation, monitoring, governance, and continuous improvement. The ideal candidate has experience operationalizing machine learning solutions in cloud environments, developing CI/CD pipelines for ML workloads, and collaborating with data scientists, data engineers, software engineers, and business stakeholders. Experience in highly regulated industries such as financial services, banking, healthcare, insurance, or telecommunications is highly desirable. 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 monitoring and observability, and reusable platform services that accelerate model development and deployment across data science teams.

Requirements

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 such as AWS, Azure, or GCP.
Experience supporting both batch and real-time machine learning solutions.
Experience working with large-scale structured and unstructured data.
Knowledge of 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, and responsible AI and model governance practices.
Experience with one or more of AWS SageMaker, Azure Machine Learning, or Google Vertex AI.
Python proficiency (required).
SQL proficiency (required).
Experience with Snowflake, Databricks, IBM DB2, or equivalent enterprise RDBMS platforms, as well as data lakes and modern cloud storage platforms.
Experience with dbt, Spark, Pandas, or PySpark.
Experience with Git/GitHub/GitLab, CI/CD tools, Terraform or infrastructure-as-code frameworks, Linux administration, and shell scripting.
Experience with Docker and Kubernetes.
Experience with Apache Kafka, event-driven architectures, or real-time inference pipelines.
Experience with Prometheus, Grafana, ELK Stack, or cloud-native monitoring solutions.

Nice To Haves

Experience with Java, Scala, or R.
Experience with OpenShift.
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 AWS, Azure, or GCP cloud certifications.
Experience working in highly regulated industries such as financial services, banking, healthcare, insurance, or telecommunications.

Responsibilities

Design, develop, deploy, and manage production-grade machine learning solutions.
Build scalable and reusable machine learning platforms supporting model development, training, validation, deployment, and monitoring.
Implement best practices for model versioning, experimentation tracking, governance, and reproducibility.
Develop frameworks for batch and real-time inference workloads.
Optimize machine learning systems for performance, reliability, maintainability, and cost efficiency.
Design and maintain automated CI/CD/CT 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 for 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 for training and inference workloads.
Optimize data availability, quality, lineage, and observability.
Implement model monitoring for 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, and 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 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.