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Machine Learning Ops Engineer

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Machine Learning Ops Engineer in USA

  • PAR Technology
  • Full time
  • Email
  • Remote

Responsibilities

  • Build and maintain automated, tested ML and data pipelines in partnership with data science teams.
  • Optimize Spark jobs and develop reliable distributed data-processing workflows.
  • Implement CI/CD workflows, automated unit and integration tests, and branch-based promotion across environments.
  • Orchestrate workflows with retries, backfills, SLA monitoring, and alerting.
  • Manage experiment tracking, model registries, lineage, and stage transitions.
  • Develop monitoring and observability for data quality, schema drift, and input/output distribution drift.
  • Build infrastructure as code, secrets management, reproducible Python environments, and cloud-native production systems.
  • Serve processed outputs to downstream systems such as Redshift for BI, activation, and attribution.
  • Support exploratory data analysis, data-quality investigations, feature pipelines, training-serving consistency, and champion/challenger evaluation.
  • Build lightweight interfaces and structured data feeds for LLM-based agent workflows while maintaining appropriate data contracts.
  • Use AI tools to accelerate coding, documentation, log analysis, dataset exploration, and evaluation-case generation.
  • Work carefully with PII-bearing datasets and support privacy-conscious entity-resolution systems.

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 Git-based workflows, automated unit and integration testing, and CI/CD promotion across development, staging, and production.
  • Experience with workflow orchestration using Airflow, Step Functions, or Databricks Workflows.
  • Familiarity with MLflow or equivalent experiment tracking and model registries.
  • Exposure to data-quality and observability 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, partitioning, joins, skew, shuffle, and spill.
  • Experience serving processed outputs to downstream systems such as Redshift.
  • Experience building feature pipelines and maintaining consistency between training and serving.
  • Fluency across the Python data science stack, including pandas, scikit-learn, and PySpark.
  • Applied statistics knowledge covering probability, hypothesis testing, sampling, and evaluation metrics.
  • Ability to perform exploratory data analysis, investigate data quality, read unfamiliar ML code, and collaborate with data science teams.
  • Interest in LLM-based agent data integration, tool definitions, retrieval patterns, and structured data interfaces.
  • 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.
  • Strong communication, collaboration, ownership, adaptability, and accountability.

Benefits

  • Remote work is available in New York, Texas, Philadelphia, Florida, North Carolina, Minnesota, Colorado, Georgia, and Illinois.
  • The role includes collaboration with cross-functional teams and exposure to cloud-native SaaS, ML operations, data engineering, and AI-agent integration work.

Salary: $125k - $160k/yr

PAR Technology

PAR Technology Corporation (NYSE: PAR) is the agentic OS for multi-unit operators, helping restaurant, retail, and high-volume commerce brands drive better performance across every location. PAR brings together mission-critical software, hardware, data, and intelligence across point of sale, digi...

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