Saviynt

identity security

PrincipalSoftwareEngineer,AIPlatformEngineering

El Segundo, California, United States FULL TIME
Market Sentiment
HIGH DEMAND

Neural analysis suggests this role is
optimal for Principal candidates.

The Brief

“Principal Software Engineer, AI Platform Engineering at Saviynt. Skills: AI Platform, Data Lake, Pipelines, Vector Databases. Set architectural direction for training data flow. Define standards for ML engineers”

What You'll Achieve.

move faster — securely and compliantly; point-in-time correctness; < 0. 1% consistency SLA; < 0. 1% consistency SLA; manage ANN query latency SLAs; own the data refresh cadence; staleness SLAs for retrieval context

Industry & Context.

identity security
Problems you'll solve

Solve challenging cloud and reliability problems at scale

Eligibility Requirements

adherence to Saviynt's information security and privacy policies

What They're Looking For.

Must Have

8+ years of data engineering at production scale, Demonstrated principal impact, Data lake ownership, Deep Spark (PySpark / Scala), Hands-on Beam / Dataflow, Schema registry experience, Orchestration at scale, Multi-tenant data architecture, Feature store operations, Vector databases, RAG data fundamentals, API transport

Nice to Have

Differential privacy or k-anonymity, Open source contributions, Familiarity with IAM / access governance data, Iceberg or Delta Lake at petabyte scale

What You'll Do.

Set architectural direction for training data flow

Define standards for ML engineers

Ensure tenant-isolated

traceable training signals

Operate AI Data Lake on GCS

Build batch pipelines

Build streaming pipelines

Manage schema registry

Operate orchestration layer

Implement multi-tenancy

Develop Data Anonymizer and Data Labeler microservices

Operate Feature store

Operate Vector database

Build RAG data pipeline

Expose data platform services via APIs

Build synthetic data pipelines

Implement data quality gates

How You'll Work.

Team & Collaboration

Collaborate with engineers in a reliability-focused culture

Full Job Description

## Description ABOUT SAVIYNT Saviynt is a leader in identity security, delivering an AI-powered platform that governs and secures access to applications, data, and business processes for global enterprises and government institutions. Built for the AI era, Saviynt helps organizations move faster — securely and compliantly.   ABOUT THE ROLE You set the architectural direction for how training data flows, evolves, and is governed across the AI Platform. You define the standards ML engineers and scientists build on, and ensure every training signal is tenant-isolated, PII-free, and traceable from source to model.   WHAT YOU'LL OWN AI Data Lake on GCS: bucket layout, raw → silver → gold tier separation, CMEK encryption, lifecycle rules Batch pipelines: Spark on Dataproc for TB-scale feature backfills, Iceberg compaction, and daily S3→GCS incremental sync Streaming pipelines: Apache Beam on Dataflow for sub-5-min CDC ingestion with exactly-once semantics and PII assertion gates Schema registry: Avro / Protobuf schema versioning, compatibility modes, and migration playbooks for safe schema evolution Orchestration: Flyte as primary DAG layer — task authoring standards, domain isolation, retry policies, DataCatalog memoization; evaluate Kubeflow Pipelines where relevant Multi-tenancy: strict per-tenant GCS prefix isolation, quota policies, and cross-tenant contamination validation Data Anonymizer and Data Labeler microservices: strip PII and attach ML labels before signals leave each customer environment Feature store: Feast offline (GCS Parquet) and online (Redis) with point-in-time correctness and < 0.1% consistency SLA Vector database: operate Pgvector (Cloud SQL) for POC and Qdrant on GKE for production-scale embedding storage; design index strategies (IVFFlat, HNSW) and manage ANN query latency SLAs RAG data pipeline: build embedding generation pipelines that chunk, encode, and upsert document embeddings into the vector store; own the data refresh cadence and staleness

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