Machine Learning Ops Engineer
PAR Technology18 days ago
Remote, United StatesMid Level
H1B sponsor
Base Salary
$125k - $160k/yr
Responsibilities
- Build and maintain automated, tested ML and data pipelines in partnership with data science teams.
- Move identity resolution models from notebooks into monitored, automated production systems.
- Optimize Spark jobs and diagnose issues involving skew, shuffle, spill, partitioning, and joins.
- Improve CI/CD workflows with automated unit and integration testing and branch-based promotion across environments.
- Implement workflow orchestration, retries, backfills, SLA monitoring, alerting, experiment tracking, model registries, and observability.
- Build feature pipelines and maintain consistency between training and serving data.
- Run exploratory data analysis and data-quality investigations, including profiling datasets and surfacing anomalies.
- Serve processed outputs to downstream systems and build lightweight interfaces for agent workflows.
- Support data contracts, privacy-conscious handling of PII-bearing data, and cost optimization across cloud and compute environments.
- Use AI coding assistants and other AI tools to accelerate coding, documentation, log analysis, dataset exploration, and evaluation 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.
- Experience with CI/CD for data or ML systems, Git-based workflows, unit and integration testing, and environment promotion.
- Experience with workflow orchestration using Airflow, Step Functions, or Databricks Workflows.
- Familiarity with MLflow or equivalent experiment tracking and model registry tools.
- Exposure to data-quality, schema-drift, and distribution-drift monitoring using tools such as Great Expectations, Deequ, or Datadog.
- Experience with infrastructure as code, secrets management, and reproducible Python environments.
- Production experience with AWS and working knowledge of Spark performance optimization.
- Experience serving processed outputs to downstream systems such as Redshift.
- Ability to support data science through EDA, feature pipelines, training-serving consistency, and applied statistical evaluation.
- Fluency across the Python data science stack, including pandas, scikit-learn, and PySpark.
- Interest in LLM-based agents, structured data interfaces, data contracts, and agent workflows.
- Strong communication, collaboration, ownership, accountability, and ability to work in a fast-moving environment.
- Experience with agentic frameworks such as LangChain or LlamaIndex is preferred.
- Exposure to PII-based record linkage, probabilistic matching, graph or clustering-based entity resolution, blocking strategies, and cloud cost optimization is preferred.
Benefits
- Remote work is available in New York, Texas, Philadelphia, Florida, North Carolina, Minnesota, Colorado, Georgia, and Illinois.
- The role includes opportunities to work with cloud-native, data-intensive applications and next-generation identity resolution systems.
- PAR provides equal employment opportunities and reasonable accommodations for individuals with disabilities.
- The company expects all roles to incorporate AI into day-to-day responsibilities, including use of AI coding assistants such as Claude Code, Cursor, or Copilot.
Tech Stack
Categories
Data EngineeringML Engineering
About PAR Technology
PAR Technology (NYSE: PAR) builds software and hardware for multi‑unit restaurants and retailers, including point of sale, digital ordering, loyalty, payments, and back‑office systems. The company sells a SaaS platform with integrated devices and services used by over 100,000 restaurant locations worldwide. Founded in 1968 and headquartered in New Hartford, NY, PAR focuses on enterprise operators and franchises that need to manage performance across many locations.