PAR Technology - Machine Learning Ops Engineer

• 2–4 years of experience building production data or ML systems, with a Bachelor’s degree in Computer Science, Data Engineering, or a related field (or equivalent practical experience) • A highly motivated, curious, and capable learner with a strong sense of ownership and accountability • A strong communicator and collaborator who works well across cross-functional teams • Comfortable in a fast-moving environment — able to shift priorities, navigate ambiguity, and support a customer-facing system • MLOps & Platform Engineering • Experience building and maintaining automated, tested ML or data pipelines in partnership with other teams (you don’t need to be a from-scratch model builder) • Experience optimizing CI/CD for data/ML: Git-based workflows, automated unit and integration tests (pytest or similar), and branch-based promotion through dev/stage/prod • Workflow orchestration with Airflow, Step Functions, or Databricks Workflows, including retries, backfills, and basic SLA monitoring and alerting • Familiarity with experiment tracking and model registries (MLflow or equivalent) for lineage and stage transitions • Exposure to monitoring and observability — data-quality and schema-drift checks (such as Great Expectations, Deequ, or Datadog) and awareness of input/output distribution drift • Infrastructure as code (Terraform preferred), secrets management, and reproducible Python environments (Docker, Poetry/uv/Conda) • Cloud & Distributed Compute • Production experience on AWS (S3, EMR, Glue, Step Functions, Lambda, IAM, CloudWatch) • Working knowledge of Spark performance — comfortable diagnosing and addressing common issues like skew, shuffle, and spill, with sensible partitioning and join strategies • Experience serving processed outputs to downstream systems (e.g., Redshift for BI, activation, or attribution) • Data Science Partnership • A strong technical partner to data science (you’re not the lead data scientist on IDR — that’s covered) • Able to run EDA and data-quality investigations: profiling datasets, surfacing anomalies, and communicating findings clearly • Experience building and maintaining feature pipelines and keeping training and serving consistent • A solid applied-stats foundation (probability, hypothesis testing, sampling, evaluation metrics) — enough to support champion/challenger evaluation and flag when something looks off • Comfortable reading ML code you didn’t write and asking good questions; fluent across the Python DS stack (pandas, scikit-learn, PySpark) • AI Data Integration • AI Data Integration • Interest in how LLM-based agents consume structured data — tool definitions, retrieval patterns, and pre-computed signals vs. raw data • Willingness to build lightweight interfaces that make IDR outputs queryable by agent workflows (MCP servers, REST/GraphQL endpoints, or structured feeds) • Good judgment about data contracts — what to expose, at what granularity, and how to keep it maintainable • Experience with agentic frameworks (LangChain, LlamaIndex, or similar) is a plus; genuine interest is the baseline • Exposure to PII-based record linkage, probabilistic matching methodology, or graph/clustering-based entity resolution (e.g., connected components) • Awareness of blocking strategies and the recall/runtime tradeoffs they drive at large record volumes • Comfort working carefully with PII-bearing datasets (our internal processes cover the privacy and compliance side) • Cost-optimization wins on EMR, AWS, Databricks, or equivalent Dev environment • AI in Your Workflow: • Beginning in FY2026, all roles at PAR Technology are expected to incorporate AI into day-to-day responsibilities. For this role, the expectation is that you will adapt AI to help drive efficiencies in your workflow, including but not limited to: • Daily, fluent use of AI coding assistants (Claude Code, Cursor, Copilot, or equivalent) as a core part of your workflow • Good judgment about when to delegate to AI vs. write by hand — scaffolding tests, drafting refactors, generating boilerplate and IaC, and explaining unfamiliar code, while writing the tricky parts yourself • Holding AI-generated code to the same review bar as your own: you read it, test it, and are accountable for it • Using AI to accelerate non-coding work too — drafting design docs, summarizing logs and runs, exploring datasets, and generating eval cases • Interview Process: • Interview Process: • Interview #1: Phone Screen with Talent Acquisition Team • Interview #2: Video interview with the Hiring Manager (via MS Teams) • Interview #3: Video interviews with the Team (via MS Teams)