Lead Machine Learning Operations Engineer

Job title: Lead Machine Learning Operations Engineer

About the Role Paramount is seeking a Lead Machine Learning Operations Engineer to own the operational excellence, observability, reliability, and governance layer around our personalization and recommendation ML systems. You will help detect model behavior changes, diagnose issues quickly, and prevent bad deployments from reaching customers. This is a lead-level IC role that sets technical direction and drives adoption across ML Engineering, DevOps, Platform Engineering, Data Engineering, and Product, partnering closely with ML engineers who own model development.

What You'll Do

• Own ML production reliability strategy: define and lead the operational strategy for production ML systems, including monitoring, traceability, deployment safety, incident response, and post-deployment validation.
• Own model traceability and governance: ensure every production model has clear lineage and drive adoption of model registry and metadata tooling across ML teams.
• Build end-to-end ML observability: design and implement monitoring across data arrival, feature freshness, distribution stability, candidate generation, ranking behavior, model metrics, serving latency, and SLA performance.
• Define production health metrics: partner with ML, data, product, and business stakeholders to define post-deployment metrics covering model quality, system reliability, business guardrails, and degradation indicators.
• Detect drift and degradation proactively: detect data drift, feature drift, model behavior changes, and silent failures before they impact customers via thresholding, alerting, anomaly detection, and release-over-release monitoring.
• Lead diagnostic tooling and root-cause analysis: build dashboards, logs, and diagnostic workflows that progress quickly from “recommendations look off” to root cause, with context across candidates, features, scores, ranking decisions, and downstream outcomes.
• Own ML deployment safety: define and operate automated gates that prevent bad models or bad data from being promoted to production; establish validation checks, rollback criteria, canary strategies, shadow testing, and release health reviews.
• Lead ML incident response: own incident response practices for ML systems, including rollback playbooks, hotfix strategies, severity definitions, tradeoff frameworks, communications, and post-mortems; drive closure of systemic gaps after incidents.
• Partner across ML Platform, Data, and ML: collaborate with DevOps/Platform on infrastructure and observability needs; with Data Engineering on data quality, drift, and freshness; and with ML Engineering to embed operational requirements into development and deployment workflows.
• Set standards and mentor others: act as the technical lead for ML operations—establish reusable patterns, playbooks, and standards, and mentor engineers on reliability, observability, and operational rigor.

What We're Looking For

• 5+ years of experience in machine learning engineering, ML platform, applied ML, MLOps, data platform, reliability engineering, or a related technical role.
• Demonstrated experience operating production ML systems, including monitoring, deployment, incident response, model validation, data quality, or reliability ownership.
• Experience leading technical initiatives across multiple engineering teams, especially where success required influencing architecture, tooling, standards, or adoption.
• Hands-on experience with model registries, feature stores, ML metadata systems, production monitoring, model deployment pipelines, or ML observability platforms.
• Solid knowledge of end-to-end ML systems, including training data, features, model artifacts, offline validation, online serving, post-deployment metrics, and business outcome measurement.
• Ability to reason about ML operational failure modes: stale features, distribution shift, training-serving skew, delayed labels, and offline-online metric gaps.
• Solid SQL skills and comfort investigating data quality, feature distributions, model outputs, pipeline behavior, and production anomalies.
• Track record of cross-functional collaboration with Platform, Data, and ML Engineering to deliver production-grade operational capabilities.
• Solid written and verbal communication skills, including the ability to explain ML system health, risks, incidents, and tradeoffs to both technical and non-technical stakeholders.

Nice to Have

• Experience operating recommendation, personalization, ranking, search, ads, content discovery, or marketplace ML systems at scale.
• Experience with real-time or near-real-time model serving systems.
• Experience with feature stores, model registries, metadata stores, experiment tracking, data quality tools, lineage systems, or observability platforms.
• Experience designing automated validation gates, canary deployments, rollback strategies, shadow deployments, or progressive delivery workflows for ML.