MLOps Engineer

Requirements:

• At least 5 years of experience in ML engineering, MLOps, data engineering, or software engineering, including 3 or more years supporting production machine-learning systems.
• Experience managing the end-to-end ML lifecycle, including deployment, orchestration, experiment tracking, versioning, monitoring, and automated retraining.
• Hands-on experience with cloud-based machine learning on AWS, Azure, or GCP, including batch and real-time solutions and large-scale data.
• Proficiency in Python and SQL; Java, Scala, or R experience is preferred.
• Familiarity with data platforms such as Snowflake, Databricks, IBM DB2, comparable relational databases, and cloud data lakes or storage.
• Experience with data transformation tools such as dbt, Spark, Pandas, or PySpark.
• Knowledge of DevOps and infrastructure tools and practices, including Git-based platforms, CI/CD tools, Terraform or other infrastructure-as-code approaches, Linux, and shell scripting.
• Experience with Docker and Kubernetes; OpenShift is preferred.
• Familiarity with Apache Kafka, event-driven architectures, and real-time inference pipelines.
• Experience with monitoring and observability tools such as Prometheus, Grafana, ELK, or cloud-native alternatives.
• Preferred: experience implementing enterprise feature stores and model registries, and applying ML governance frameworks.
• Preferred: experience with real-time decisioning or scoring platforms for areas such as credit risk, fraud, customer analytics, recommendations, or predictive analytics.
• Preferred: familiarity with MLOps platforms such as MLflow, Kubeflow, Airflow, Argo Workflows, Feast, or SageMaker Pipelines.
• Preferred: knowledge of software architecture patterns, distributed systems, AI/ML regulatory frameworks, and model risk management.
• Preferred: relevant AWS, Azure, or GCP certifications.

Responsibilities:

• Design, build, deploy, and manage production-grade machine-learning platforms and solutions for development, training, validation, deployment, and monitoring.
• Establish model versioning, experiment tracking, governance, and reproducibility for batch and real-time inference.
• Create CI/CD/CT pipelines that automate testing, validation, deployment, rollback, and retraining, using infrastructure-as-code practices.
• Design and support enterprise feature stores, integrating machine learning with data warehouses, data lakes, and operational data sources.
• Develop scalable data pipelines for training and inference, emphasizing data quality, lineage, and observability.
• Monitor model performance, data quality, data and concept drift, feature drift, and system health; configure alerts and support incident management.
• Build low-latency scoring platforms and event-driven architectures for near-real-time decision-making.
• Put governance, security, privacy, and compliance controls in place, with auditability, explainability, and lineage tracking.
• Work with cross-functional partners, mentor junior engineers, and encourage modern machine-learning engineering practices.

Technologies:

• AI
• Airflow
• AWS
• Azure
• CI/CD
• Cloud
• Databricks
• DevOps
• Docker
• ELK
• GCP
• Git
• Grafana
• IBM
• IBM Db2
• Incident Management
• Support
• Java
• Kafka
• Kubeflow
• Kubernetes
• Linux
• Machine Learning
• MLflow
• MLOps
• OpenShift
• Prometheus
• Python
• PySpark
• SQL
• Scala
• Security
• Snowflake
• Spark
• Terraform
• dbt
• pandas
• GitHub
• GitLab
• Jenkins

More

We are BCforward, a global IT consulting and workforce solutions firm serving Fortune 500 and government clients. Founded in 1998, we have grown into a full-service business solutions provider, with offices and delivery centers across North America and India. We are hiring a Senior Machine Learning Operations (MLOps) Engineer for a 14-month contract in Cincinnati, Ohio, at $80–$85 per hour on W-2. The role focuses on scalable platforms and production ML systems, with collaboration across data science, data engineering, software engineering, and business teams; experience in regulated sectors such as financial services, healthcare, insurance, or telecommunications is relevant. We offer competitive compensation and benefits, growth opportunities with global clients, a supportive and inclusive culture, and exposure to advanced technologies and projects. We are an equal opportunity employer committed to an inclusive workplace and consider qualified applicants without regard to protected characteristics.

last updated 43 week of 2026