Machine Learning Ops Engineer

This is a fully remote position, open to applicants in Colorado, +7 more states.

📋 Description

• Transition Bridg’s identity resolution model from notebooks to a monitored and automated production environment.

• Develop and sustain reliable, scalable data and machine learning (ML) pipelines.

• Create tests and implement observability for match data.

• Optimize Spark jobs and establish CI/CD workflows.

• Enhance system testing and documentation practices.

• Collaborate with data science teams on production ML systems.

• Conduct exploratory data analysis and investigations into data quality.

• Maintain feature pipelines and ensure consistency between training and serving phases.

• Assess champion/challenger models and identify any anomalous results.

• Construct lightweight interfaces to enable queryable identity-resolution outputs for agent workflows.

• Utilize AI for coding, refactoring, infrastructure as code, design documentation, log/run summarization, dataset exploration, and evaluation-case generation.

• Report directly to the Director of Product at Bridg.

⛳️ Requirements

• 2–4 years of experience in developing production data or ML systems.

• Bachelor's degree in Computer Science, Data Engineering, or a related field, or equivalent practical experience.

• Proven experience in building and maintaining automated, tested ML or data pipelines.

• Knowledge of optimizing CI/CD processes for data/ML, including Git-based workflows, automated unit and integration tests, and branch-based promotion through development, staging, and production environments.

• Experience with workflow orchestration tools like Airflow, Step Functions, or Databricks Workflows, covering retries, backfills, SLA monitoring, and alerting.

• Familiarity with experiment tracking and model registries such as MLflow.

• Exposure to monitoring and observability, including data quality and schema drift checks, as well as input/output distribution drift.

• Experience with infrastructure as code, secrets management, and creating reproducible Python environments.

• Production experience with AWS services, including S3, EMR, Glue, Step Functions, Lambda, IAM, and CloudWatch.

• Understanding of Spark performance issues, including skew, shuffle, spill, partitioning, and join strategies.

• Experience in serving processed outputs to downstream systems such as Redshift.

• Ability to conduct exploratory data analysis and data-quality investigations.

• Experience in building and maintaining feature pipelines while ensuring consistency in training and serving.

• Solid foundation in applied statistics, including probability, hypothesis testing, sampling, and evaluation metrics.

• Proficiency in the Python data science ecosystem, including libraries like pandas, scikit-learn, and PySpark.

• Interest in LLM-based agent data integration and eagerness to develop interfaces such as MCP servers, REST/GraphQL endpoints, or structured feeds.

• Excellent judgment regarding data contracts.

• Comfort in working with datasets containing personally identifiable information (PII).

• Regular use of AI coding assistants and responsibility for reviewing and testing AI-generated code.

• Must be legally authorized to work in the U.S. or Canada.

• No restrictive covenant agreement that would prevent employment at PAR.

🏝️ Benefits

• Eligibility for bonuses.

• Potential for additional compensation, which may include commission and/or equity, as applicable.

• Flexible remote work options in eligible locations.

• Reasonable accommodations available for individuals with disabilities.