Remote MLOps Engineer - Cloud Data Pipelines & MLflow

PAR Technology; New York (NY); Remote; USD 125,000 - 160,000; Full time

Benefits offered by this job

Remote work

Bonus potential

Job summary

PAR Technology is seeking an ML Ops Engineer for Bridg, focused on taking data science work from notebooks to production-scale pipelines on AWS and Snowflake. You will build and maintain data and ML pipelines, tune Spark jobs, and strengthen CI/CD with robust tests and observability.

You’ll partner with a data science team in a fast-moving SaaS environment, applying MLflow, Terraform, Docker, and modern tooling to deliver reliable, scalable IDR/data integration solutions.

Qualifications

• 2–4 years of experience building production data or ML systems with a related degree.
• A highly motivated, curious learner with strong ownership and accountability.
• Strong communicator and collaborator across cross-functional teams.
• Comfortable in a fast-moving environment and supporting a customer-facing system.

Responsibilities

• Build and maintain automated, tested ML or data pipelines in partnership with other teams.
• Optimize CI/CD for data/ML: Git workflows, automated tests, and branch-based promotion.
• Orchestrate workflows with Airflow, Step Functions, or Databricks Workflows, including retries and monitoring.
• Utilize MLflow or equivalents for experiment tracking and model registries.
• Implement data quality checks and monitoring (Great Expectations, Deequ, Datadog).
• Maintain reproducible Python environments (Docker, Poetry/uv/Conda).

Skills

Python

Spark

CI/CD for ML

Data pipelines

Distributed computing

Pandas/Scikit-learn

Monitoring/observability

Databricks

Education

Bachelor’s degree in Computer Science, Data Engineering, or related field

Tools

Spark

Airflow

MLflow

Terraform

Docker

Python

Snowflake

AWS

Databricks

Job description

PAR Technology is seeking an ML Ops Engineer for Bridg, focused on taking data science work from notebooks to production-scale pipelines on AWS and Snowflake. You will build and maintain data and ML pipelines, tune Spark jobs, and strengthen CI/CD with robust tests and observability.

You’ll partner with a data science team in a fast-moving SaaS environment, applying MLflow, Terraform, Docker, and modern tooling to deliver reliable, scalable IDR/data integration solutions.