MLOps engineer

BC Forward; Cincinnati (OH); On-site; USD 110,000 - 117,000; Full time

Job summary

BCforward is seeking a Senior MLOps Engineer to design, build, and optimize scalable ML platforms and production systems in Cincinnati. The role focuses on cloud-based ML operations, CI/CD for ML workloads, and cross-functional collaboration with data science, data engineering, and software teams.

Qualifications include 5+ years in ML/MLOps, end-to-end lifecycle management, and cloud experience on AWS/Azure/GCP. Proficiency in Python, SQL, Docker, Kubernetes, and Terraform is desired.

Qualifications

• 5+ years in ML Engineering, MLOps, or related fields.
• End-to-end ML lifecycle management including deployment, orchestration, and monitoring.
• Cloud ML experience on AWS, Azure, or GCP with batch and real-time solutions.

Responsibilities

• Design, deploy, and manage production-grade ML platforms and pipelines.
• Implement model versioning, experiment tracking, and governance for reproducibility.
• Build CI/CD pipelines for ML with IaC and automated retraining.
• Develop data pipelines for training and inference with emphasis on quality and observability.
• Establish monitoring for model performance and data drift with alerting.

Skills

Python

SQL

Cloud ML

Experiment tracking

Tools

Docker

Kubernetes

Git/GitHub

Terraform

Job description

Job Title: Senior Machine Learning Operations (MLOps) EngineerLocation: Cincinnati OHDuration: Contract - 14 monthsPay Range: $80/hr to $85/hr (W2)Job ID: 411247

About BCforward

BCforward is a leading global IT consulting and workforce solutions firm providing services and support to Fortune 500 and government clients. Founded in 1998, BCforward has grown with our customers needs into a full-service business solutions provider. With delivery centers and offices across North America and India, we take pride in building long-term relationships and delivering excellence through innovation, collaboration, and integrity.

Job Description

We are seeking a senior MLOps engineer to design, build, and optimize scalable platforms and production ML systems. The ideal candidate will have strong experience with cloud-based ML operations, CI/CD for ML workloads, and collaboration across data science, data engineering, software engineering, and business stakeholders, with exposure to regulated industries such as financial services, banking, healthcare, insurance, or telecommunications.

Responsibilities

• Design, develop, deploy, and manage production-grade ML solutions and platforms for development, training, validation, deployment, and monitoring.
• Implement model versioning, experiment tracking, governance, and reproducibility for batch and real-time inference.
• Build CI/CD/CT pipelines to automate testing, validation, deployment, rollback, and retraining with infrastructure-as-code practices.
• Design and support enterprise feature stores and integrate ML with data warehouses, data lakes, and operational sources.
• Develop scalable data pipelines for training and inference with a focus on data quality, lineage, and observability.
• Implement monitoring for model performance, data quality, data and concept drift, feature drift, and system health with alerting and incident management.
• Build low-latency scoring platforms and event-driven architectures to enable near real-time decisioning.
• Establish MLOps governance, security, privacy, and compliance controls with auditability, explainability, and lineage tracking.
• Collaborate with cross-functional partners, mentor junior engineers, and drive adoption of modern ML engineering practices.

Required Skills & Qualifications

• 5+ years in ML Engineering, MLOps, Data Engineering, or Software Engineering, including 3+ years supporting production ML systems.
• End-to-end ML lifecycle management, including deployment, orchestration, experiment tracking, versioning, monitoring, and automated retraining.
• Cloud ML experience on AWS, Azure, or GCP with batch and real-time solutions and large-scale data.
• Python and SQL proficiency. Java, Scala, or R preferred.
• Data platforms: Snowflake, Databricks, IBM DB2 or similar RDBMS, and cloud data lakes/storage.
• Data transformation: dbt, Spark, Pandas, PySpark.
• DevOps and infrastructure: Git/GitHub/GitLab, Jenkins/GitHub Actions/Azure DevOps, Terraform or IaC, Linux, shell scripting.
• Containers and orchestration: Docker, Kubernetes, OpenShift preferred.
• Streaming and real-time: Apache Kafka, event-driven architectures, real-time inference pipelines.
• Monitoring and observability: Prometheus, Grafana, ELK, or cloud-native solutions.

Preferred Skills

• Enterprise feature store implementation and model registry design with ML governance frameworks.
• Real-time decisioning or scoring platforms for credit risk, fraud, customer analytics, recommendations, or predictive analytics.
• MLOps platforms: MLflow, Kubeflow, Airflow, Argo Workflows, Feast, SageMaker Pipelines, or similar.
• Knowledge of software architecture patterns, distributed systems, and AI/ML regulatory frameworks and model risk management.
• Relevant cloud certifications on AWS, Azure, or GCP.

Why BCforward?

• Competitive compensation and benefits.
• Opportunities for growth with global clients.
• A supportive, inclusive culture that values innovation and people.
• Exposure to cutting-edge technologies and projects.

About Our Commitment

BCforward is an equal opportunity employer. We value diversity and are committed to creating an inclusive environment for all employees. All qualified applicants will receive consideration for employment without regard to race, color, religion, gender, sexual orientation, gender identity, national origin, age, disability, or veteran status.