MLOps Engineer resume example

• Packaged a Python machine learning service in Docker and added CI/CD checks for model, schema, and dependency versions; made deployment reproducible from a recorded artifact rather than a notebook state.
• Added monitoring for prediction errors, input drift, and inference latency; tested a rollback to the previous model and verified that feature versions remained compatible after the switch.
• Linked model-registry entries to training runs and feature definitions, tested canary serving against the incumbent model, and documented rollback criteria for data drift.
• Owned model-release validation using Docker and CI/CD tests, resolved a training-serving schema mismatch and completed rollback verification before deployment.
• Built Python monitoring using SQL feature checks and Spark ETL logs, reduced drift investigation from 60 to 25 minutes and published the model-health dashboard.

• Scheduled GPU workloads from utilization and priority data. Kept the lower-effort process in place after handoff.
• Investigated a discrepancy between a published metric and its source records, traced the transformation that changed the population, and corrected the calculation with a reproducible query.
• Compared the last successful data refresh with a failed run, separated missing source data from transformation errors, and reran only the affected interval. Kept the original query and corrected result together for review.

• Added canary inference and automatic rollback to 6 online models
• Generated synthetic source records with duplicates, late arrivals and corrected values; wrote assertions for row counts and key uniqueness, and recorded the expected effect of each case on the reported metric.
• Compared the analytical output with a manually calculated reference table, traced differences to a transformation step, and retained a data dictionary and rerun instructions alongside the corrected query.
• Owned the synthetic-data validation using a manually calculated reference, resolved duplicate-key inflation and completed a notebook that reproduces the corrected totals.

Relevant coursework: Algorithms, operating systems, databases, computer networks

Model deployment · Training-serving consistency · Model monitoring · Rollback testing

• Published an independent methods note using reproducible queries, explaining the data grain, excluded records and sensitivity of the result to a changed denominator.