Senior AIOps ML Engineer

Role: Senior AIOps ML Engineer
Location: Woodland Hills CA

Descriptions:
Core Responsibilities
Lakehouse Architecture & Data Engineering
Schema Design: Design and evolve the Lakehouse schema (Delta Lake / Apache Iceberg) for multi-domain observability data at petabyte scale.
Pipeline Engineering: Build and maintain robust ingestion pipelines from the OTel Collector through Kafka to the Lakehouse ensuring exactly-once semantics and strict schema enforcement.
Data Transformation: Implement dbt transformation models to generate mart-ready denormalized fact and dimension tables for each of the six domains.
Data Quality Governance: Define and enforce data quality contracts establishing SLAs for data freshness completeness and cardinality budgets per mart.
Performance Optimization: Optimize query performance utilizing partitioning strategies Z-ordering bloom filters and materialized views tailored for time-series patterns.
ML Model Development & AIOps
AIOps Modeling: Design train and deploy machine learning models for streaming multivariate anomaly detection root-cause analysis and incident forecasting across all six mart domains.
Streaming Inference: Build low-latency streaming inference pipelines (Flink / Spark Streaming) for real-time anomaly scoring on APM infrastructure and security signals.
Log Intelligence: Develop sophisticated log intelligence modelsincluding clustering (DRAIN3 / LogBERT) NLP classification and error deduplicationover the Log mart.
Behavioral Analytics: Implement unsupervised and semi-supervised methods for User Experience frustration detection and KPI correlation analysis.
Feature Store Management: Own the ML feature store managing feature engineering versioning backfill pipelines and point-in-time correct joins for training datasets.
Model Lifecycle MLOps: Instrument model performance tracking including drift detection accuracy monitoring and automated retraining triggers.
AIOps Platform & Productionization
Workflow Orchestration: Design and operate the end-to-end AIOps workflow spanning signal ingestion feature computation model inference alert routing and auto-remediation hooks.
Model Serving Infrastructure: Build high-performance model serving infrastructuresupporting real-time REST/gRPC endpoints and async batch scoringwith strict p99 latency SLOs.
Incident Tool Integration: Integrate AIOps insights with incident management platforms (PagerDuty Opsgenie) and internal runbooks to deliver enriched noise-reduced alerting.
Business Impact Quantification: Define and publish metrics from the Business KPI mart to quantify the blast radius revenue loss and affected user counts for each incident.
Security & Compliance Observability
Security Mart Collaboration: Partner with the Security team to build the Security mart schema including threat feed ingestion UEBA baselines and CVE correlation pipelines.
Threat Detection: Train anomalous-access and lateral-movement detection models tuning precision/recall thresholds in collaboration with the SOC team.
Compliance & Governance: Ensure all data handling across the marts adheres strictly to data residency requirements PII masking standards and audit-log protocols.
Collaboration & Engineering Standards
Schema Contracts: Define telemetry schema contracts with the OTel Instrumentation team to guarantee high upstream signal quality for downstream ML models.
Organizational Standards: Author ML platform RFCs and contribute actively to observability data model standards across the broader engineering organization.
Mentorship & Reviews: Mentor junior ML and data engineers and conduct rigorous design reviews for new mart schemas and model architectures.