AI Ops ML Engineer - W2 only

The following requirement is open with our client.                            

Title                                     : AI Ops ML Engineer

Location                             : Woodland Hills, CA(Onsite)

Pay rate                              : $60 - $64/hr on W2

Duration                            : 12+ Months

Relevant Experience (in Yrs.): 10+yrs.

Role 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 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 (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.

Must Have Skills:

AI Ops ML Engineer

Lakehouse, Delta Lake, Kafka,

Data Governance, Spark, KPI,

Thanks & regards, 

Naveen Yarramsetty

Technical Recruiter | ASCII Group, LLC
Office: 

Email: 

38345 W. 10 Mile Rd, Ste.#365; Farmington, MI  48335 

Website: