Senior AIOpsML Engineer

Overview

Job Description Senior AIOpsML Engineer. Must Have Technical/Functional Skills. This position involves building and scaling a best-in-class AIOps function designed to transform raw observability signals into automated intelligence. As a Senior AIOps ML Engineer, the successful candidate will own the platform's intelligence layer—architecting and operating the Lakehouse, engineering six purpose-built data marts, and training machine learning models to power anomaly detection, root-cause analysis, forecasting, and auto-remediation.

Operating at the intersection of data engineering, machine learning, and observability, this role requires translating high-cardinality telemetry from the Open Telemetry (OTel) pipeline into structured, query-optimized mart schemas, and developing the models that make those datasets actionable.

• 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 sop histicated log intelligence models—including clustering (DRAIN3 / LogBERT), NLP classification, and error deduplication—over 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 infrastructure—supporting real-time REST/gRPC endpoints and async batch scoring—with strict p99 latency SLOs.
  • Incident Tool Integration:
    Integrate AIOps insights with incident management platforms (Pager Duty, 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.

Salary Range- $120,000-$130,000 a year