Machine Learning Ops Engineer

PAR Technology

6d•$125,000 - $160,000•Remote

About The Position

As an ML Ops Engineer at Bridg, you will partner with a data science team to move its next-generation identity resolution model from notebooks into monitored, automated production. You will build and maintain pipelines, tests, and observability systems that keep match data reliable at scale. Most of your week will involve writing code, including building pipelines, tuning Spark jobs, implementing CI/CD, and improving system testing and documentation. The organization builds cloud-native, data-intensive SaaS applications on AWS and Snowflake, using Spark, Python, MLflow, and related tools. The role calls for a curious, collaborative engineer with ownership and accountability who can work effectively in a fast-moving environment. The position is remote in the listed eligible locations. The role also expects daily use of AI tools to improve coding and other work, with the engineer accountable for reviewing and testing AI-generated output.

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 testing (pytest or similar), and branch-based promotion through development, staging, and production.
Experience with workflow orchestration using Airflow, Step Functions, or Databricks Workflows, 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 such as Docker, Poetry/uv, or Conda.
Production experience on AWS, including services such as S3, EMR, Glue, Step Functions, Lambda, IAM, and CloudWatch.
Working knowledge of Spark performance and common performance issues, partitioning, and join strategies.
Experience serving processed outputs to downstream systems.
Ability to run exploratory data analysis and data-quality investigations, profile datasets, surface anomalies, and communicate findings clearly.
Experience building and maintaining feature pipelines and keeping training and serving consistent.
Applied-statistics foundation covering probability, hypothesis testing, sampling, and evaluation metrics.
Comfort reading ML code written by others and asking questions; fluency with Python data science tools such as pandas, scikit-learn, and PySpark.
Interest in how LLM-based agents consume structured data, including tool definitions, retrieval patterns, and precomputed signals versus raw data.
Willingness to build lightweight interfaces that expose identity-resolution outputs to agent workflows.
Good judgment about data contracts, including data granularity and maintainability.
Curiosity, strong ownership and accountability, communication and collaboration skills, and comfort working across teams in a fast-moving environment with shifting priorities and ambiguity.
Daily, fluent use of AI coding assistants as part of the workflow, with judgment about when to delegate to AI and responsibility for reviewing and testing generated code.

Nice To Haves

Experience with agentic frameworks such as LangChain or LlamaIndex; genuine interest in the area is expected.
Exposure to PII-based record linkage, probabilistic matching methodology, or graph/clustering-based entity resolution such as connected components.
Awareness of blocking strategies and their recall/runtime tradeoffs at large record volumes.
Careful experience working with PII-bearing datasets.
Cost-optimization wins on EMR, AWS, Databricks, or an equivalent development environment.
Terraform is preferred for infrastructure as code.

Responsibilities

Build and maintain automated, tested ML or data pipelines in partnership with other teams.
Move the identity resolution model from notebooks to monitored, automated production.
Tune Spark jobs and diagnose performance issues such as skew, shuffle, and spill; use appropriate partitioning and join strategies.
Optimize CI/CD workflows for data and ML systems, including automated testing and branch-based promotion through development, staging, and production.
Implement workflow orchestration, retries, backfills, SLA monitoring, and alerting.
Use experiment tracking and model registries to support lineage and model stage transitions.
Build monitoring and observability, including data-quality, schema-drift, and input/output distribution checks.
Build and 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.
Support champion/challenger evaluation and identify issues using applied statistical methods.
Serve processed outputs to downstream systems such as Redshift for BI, activation, or attribution.
Build lightweight interfaces that make identity-resolution outputs queryable by agent workflows, such as MCP servers, REST or GraphQL endpoints, or structured feeds.
Apply sound judgment to data contracts, including decisions about exposed data granularity and maintainability.
Use AI tools in day-to-day work to accelerate coding and non-coding tasks, while reviewing, testing, and taking accountability for generated work.
Improve the testing and documentation of the systems maintained.