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Machine Learning Ops Engineer
PAR TechnologyPosted 10/21/2026full-timeRemote • United StatesJuniorMid-Level💰 $125,000 - $160,000 per yearWebsite
Core Competencies
Role fitCore Competencies
Use this summary to align your resume positioning with the role.
Demonstrates expertise in building and maintaining automated, scalable data and ML pipelines, with a strong foundation in Python and experience in production environments on AWS. Proficient in CI/CD workflows, data quality investigations, and applying AI to enhance coding and infrastructure processes.
Highest-signal resume keywords
Production Data Or ML Systems ExperienceCI/CD Optimization For Data/MLWorkflow Orchestration Using AirflowFluency In Python Data Science StackExperience With AWS Services
Tech Stack
Tools & technologiesAbout the role
Key responsibilities & impact- Take Bridg’s identity resolution model from notebooks to monitored, automated production
- Build and maintain reliable, scalable data and ML pipelines
- Write tests and implement observability for match data
- Tune Spark jobs and build CI/CD workflows
- Improve system testing and documentation
- Partner with data science on production ML systems
- Run exploratory data analysis and data-quality investigations
- Maintain feature pipelines and consistency between training and serving
- Evaluate champion/challenger models and identify anomalous results
- Build lightweight interfaces to make identity-resolution outputs queryable by agent workflows
- Apply AI to coding, refactoring, infrastructure as code, design documentation, log/run summarization, dataset exploration, and evaluation-case generation
- Report to the Director of Product, Bridg
Requirements
What you’ll need- 2–4 years of experience building production data or ML systems
- Bachelor’s degree in Computer Science, Data Engineering, or a related field, or equivalent practical experience
- Experience building and maintaining automated, tested ML or data pipelines
- Experience optimizing CI/CD for data/ML, including Git-based workflows, automated unit and integration tests, and branch-based promotion through dev/stage/prod
- Experience with workflow orchestration using Airflow, Step Functions, or Databricks Workflows, including retries, backfills, SLA monitoring, and alerting
- Familiarity with experiment tracking and model registries such as MLflow
- Exposure to monitoring and observability, data-quality and schema-drift checks, and input/output distribution drift
- Experience with infrastructure as code, secrets management, and reproducible Python environments
- Production experience on AWS, including S3, EMR, Glue, Step Functions, Lambda, IAM, and CloudWatch
- Working knowledge of Spark performance, including skew, shuffle, spill, partitioning, and join strategies
- Experience serving processed outputs to downstream systems such as Redshift
- Ability to run EDA and data-quality investigations
- Experience building and maintaining feature pipelines and keeping training and serving consistent
- Applied statistics foundation in probability, hypothesis testing, sampling, and evaluation metrics
- Fluency across the Python data science stack, including pandas, scikit-learn, and PySpark
- Interest in LLM-based agent data integration and willingness to build interfaces such as MCP servers, REST/GraphQL endpoints, or structured feeds
- Good judgment about data contracts
- Comfort working with PII-bearing datasets
- Daily use of AI coding assistants and accountability for reviewing and testing AI-generated code
- Must be legally authorized to work in the U.S. or Canada
- No restrictive covenant agreement preventing employment at PAR
Benefits
Comp & perks- Bonus eligibility
- Potential additional compensation, including commission and/or equity, as applicable
- Remote work arrangement in eligible locations
- Reasonable accommodations for individuals with disabilities
ATS Keywords
✓ Tailor your resumeApplicant Tracking System Keywords
Tip: use these terms in your resume and cover letter to boost ATS matches.
Hard Skills
Data Pipeline DevelopmentMachine Learning SystemsCI/CD WorkflowsSpark Performance OptimizationExploratory Data AnalysisInfrastructure As CodeApplied StatisticsFeature Pipeline MaintenanceAutomated TestingData Quality Checks
Soft Skills
Good Judgment About Data ContractsAccountability For AI-Generated Code
Tools & Technologies
AWSAirflowMLflowDatabricks WorkflowsPythonPandasScikit-learnPySparkGitCloudWatch
Industry Keywords
Identity ResolutionData QualitySchema DriftMonitoring And ObservabilityPII-Bearing DatasetsExperiment TrackingModel RegistriesChampion/Challenger ModelsAutomated Unit TestsData Engineering