Machine Learning Ops Engineer

PAR Technology•Champaign, IL

1d•Remote

About The Position

PAR Technology is hiring an ML Ops Engineer at Bridg to partner with its data science team and move a next-generation identity resolution model from notebooks into monitored, automated production. The role focuses on building and maintaining reliable data and ML pipelines, tests, CI/CD, and observability for match data at scale. Most of the work week will involve writing code, including building pipelines, tuning Spark jobs, improving CI/CD, and strengthening system testing and documentation. The position is part of a growing SaaS organization developing cloud-native, data-intensive applications with AWS, Snowflake, Spark, Python, MLflow, and related tools. The engineer will collaborate across teams, support a customer-facing system, and help data scientists with productionization, data-quality investigations, feature pipelines, and evaluation. The role also involves exploring ways to make identity-resolution outputs accessible to LLM-based agent workflows through suitable interfaces and data contracts. The position is remote in New York, Texas, Philadelphia, Florida, North Carolina, Minnesota, Colorado, Georgia, or Illinois, and reports to the Director of Product, Bridg. Beginning in FY2026, PAR expects employees in all roles to incorporate AI into their day-to-day work; for this position, that includes using AI coding assistants and other AI tools responsibly and reviewing AI-generated work to the same standard as personally written work.

Requirements

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 in partnership with other teams.
Experience with Git-based workflows, automated unit and integration tests such as pytest, and branch-based promotion through development, staging, and production.
Experience with workflow orchestration using Airflow, Step Functions, Databricks Workflows, or similar, including retries, backfills, and basic SLA monitoring and alerting.
Familiarity with experiment tracking and model registries such as MLflow or equivalent.
Exposure to monitoring and observability, including data-quality and schema-drift checks and awareness of input/output distribution drift.
Experience with infrastructure as code, secrets management, and reproducible Python environments.
Production experience on AWS.
Working knowledge of Spark performance and common optimization issues, partitioning, and join strategies.
Experience serving processed outputs to downstream systems.
Ability to perform exploratory data analysis and data-quality investigations.
A solid applied-statistics foundation, including probability, hypothesis testing, sampling, and evaluation metrics.
Comfort reading ML code written by others and fluency across the Python data science stack, including pandas, scikit-learn, and PySpark.
Interest in how LLM-based agents consume structured data and willingness to build lightweight interfaces for agent workflows.
Good judgment about data contracts and maintaining them over time.
Curiosity, strong ownership and accountability, clear communication, and ability to collaborate across cross-functional teams.
Comfort working in a fast-moving environment, shifting priorities, navigating ambiguity, and supporting a customer-facing system.
Beginning in FY2026, willingness to use AI tools as part of day-to-day work and to review and test AI-generated output.

Nice To Haves

Experience with agentic frameworks such as LangChain or LlamaIndex.
Exposure to PII-based record linkage, probabilistic matching, or graph- and clustering-based entity resolution such as connected components.
Awareness of blocking strategies and their recall/runtime tradeoffs at large record volumes.
Experience working carefully with PII-bearing datasets.
Cost-optimization experience on EMR, AWS, Databricks, or an equivalent platform.
Experience with Terraform.
Experience with Docker, Poetry, uv, or Conda for reproducible Python environments.
Experience with AWS services including S3, EMR, Glue, Step Functions, Lambda, IAM, and CloudWatch.
Experience with data-quality tools such as Great Expectations, Deequ, or Datadog.
Experience building interfaces using MCP servers, REST/GraphQL endpoints, or structured feeds.

Responsibilities

Build and maintain automated, tested ML and data pipelines in partnership with data science and other teams.
Move the identity resolution model from notebooks into monitored, automated production.
Build and optimize CI/CD workflows, automated tests, and branch-based promotion through development, staging, and production.
Configure workflow orchestration, including retries, backfills, SLA monitoring, and alerting.
Tune Spark jobs and diagnose common performance issues such as skew, shuffle, and spill.
Implement monitoring and observability, including data-quality, schema-drift, and input/output distribution checks.
Maintain feature pipelines and help keep training and serving consistent.
Run exploratory data analysis and data-quality investigations, profile datasets, surface anomalies, and communicate findings.
Partner with data science on champion/challenger evaluation and identify when results or data look unusual.
Serve processed outputs to downstream systems such as Redshift.
Build lightweight interfaces or structured feeds that make identity-resolution outputs queryable by agent workflows.
Apply sound judgment to data contracts, including deciding what data to expose and at what granularity.
Use AI tools to accelerate coding and non-coding work while reviewing, testing, and taking accountability for generated output.