---
title: 'Principal Software Engineer, AI Platform Engineering at Saviynt'
canonical: 'https://feeny.ai/job/principal-software-engineer-ai-platform-engineering-saviynt-milpitas-sfjpze0j7wrx'
type: 'job'
last_seen: '2026-09-07'
---

# Principal Software Engineer, AI Platform Engineering at Saviynt

- **Company:** Saviynt
- **Location:** Milpitas, CA
- **Compensation:** $274k–$304k
- **Employment:** full-time
- **Work type:** hybrid
- **Posted:** 2026-04-28
- **Last confirmed live:** 2026-09-07
- **Apply:** https://jobs.lever.co/saviynt/ebb9bb76-6390-4cfb-af4d-a170a9f2cfbc

## Job 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 SLAs for retrieval context Service APIs: expose data platform services (feature serving, embedding upsert, schema validation) over HTTPS with mTLS and gRPC where low-latency streaming is required Synthetic data pipelines for dev/staging where real customer data is not permitted Data quality gates: Great Expectations / dbt checks as Flyte tasks, blocking on schema and PII-absence failures

## YOU'LL THRIVE HERE IF YOU HAVE

1+ years of experience as a Principal SWE at a SaaS company Demonstrated principal impact: platform standards you defined adopted org-wide, or major cross-team pipeline/schema migrations you led Data lake ownership (essential): you have designed and operated a production data lake end-to-end — storage layout, partitioning strategy, tiered retention (hot/warm/cold), table format (Iceberg or Delta Lake), compaction, and access control; not just consumed one Deep Spark (PySpark / Scala): executor tuning, shuffle diagnosis, Iceberg table maintenance Hands-on Beam / Dataflow: windowing, exactly-once, side inputs, autoscaling Schema registry experience: Protobuf / Avro compatibility rules, breaking-change migrations in production Orchestration at scale: Flyte, Kubeflow Pipelines, Airflow, or Prefect — operated in production, ideally benchmarked two Multi-tenant data architecture: per-tenant isolation as a hard requirement, not a post-hoc concern Feature store operations: Feast or Tecton, point-in-time joins, online/offline consistency Vector databases: Pgvector or Qdrant in production — index tuning, ANN search, embedding upsert pipelines RAG data fundamentals: chunking strategies, embedding model selection, retrieval quality evaluation, and context freshness management API transport: gRPC and HTTPS/mTLS for service-to-service communication; comfortable defining proto contracts and managing certificate lifecycle Bachelor's degree in Computer Science, Engineering, or a related field, or equivalent practical experience or equivalent military experience

## NICE TO HAVE

Differential privacy or k-anonymity for ML training datasets Open source contributions: Feast, Great Expectations, Apache Beam, or dbt Familiarity with IAM / access governance data: entitlements, provisioning events, access graphs Iceberg or Delta Lake at petabyte scale

## WHY JOIN SAVIYNT

Work on a large-scale, Kubernetes-based SaaS platform Solve challenging cloud and reliability problems at scale Collaborate with strong engineers in a reliability-focused culture Competitive compensation, benefits, and growth opportunities

## SECURITY & COMPLIANCE

This role requires adherence to Saviynt's information security and privacy policies, including annual security training.

## About Saviynt

## Company Overview
- **One-liner**: Saviynt provides an AI-powered identity security and access management platform that secures and governs every identity—human, non-human, and AI agent—across the entire digital ecosystem.
- **Entity Type**: Private (Series B)
- **Headquarters**: El Segundo, California, United States
- **Founded**: 2010
- **Founders**: Not publicly available

## Core Business
- **Primary Industry**: Identity Security & Access Management (IAM/IGA) / Cybersecurity
- **Target Customers**: Large enterprises, Fortune 500 companies, government institutions (B2B, Enterprise)
- **Mission or Purpose**: To safeguard enterprises through intelligent, cloud-first identity governance and access management solutions, helping organizations safely accelerate AI deployment while protecting digital assets and driving operational efficiency.

## Products & Services
- **Saviynt Identity Cloud (Platform)**: An AI-powered platform that manages and governs human, non-human, and AI agent access to applications, data, and business processes across hybrid, cloud, and on-premises environments. It combines Identity Governance and Administration (IGA), Privileged Access Management (PAM), and Application Access Governance (AAG) into a single platform. **Type**: SaaS Platform.

## Market Standing
- **Valuation**: Not disclosed (privately held)
- **Key Metric**: Total Funding of $1.075 billion across multiple rounds. Annual Revenue is $89.3 million.
- **Notable Investors/Partners**: Kohlberg Kravis Roberts & Co. (lead, Series B), HPS Investment Partners, PNC Bank, Carrick Capital Partners. The company serves Fortune 500 companies and government institutions.
- **Growth Signals**: 1,260 employees (+28.0% YoY), 358,442 LinkedIn followers (+86.7% YoY), active job postings up +51.0% year-over-year, global expansion with offices in the US, UK, Australia, Canada, and India.

## Competitive Advantages
- **Unified Platform**: Combines IGA, PAM, and AAG into a single cloud-native platform, reducing complexity and cost for enterprises compared to multi-vendor solutions.
- **AI-Native Architecture**: Built for the AI age, it specifically addresses identity security for AI agents and non-human identities, a rapidly growing market need.
- **Enterprise Scale & Trust**: Designed for the largest, most complex organizations (Fortune 500, government) with a focus on compliance, security, and operational efficiency.
- **Strong Financial Backing**: $1.075 billion in total funding provides significant resources for R&D and market expansion.

## Strategic Focus
- **AI-Driven Identity Security**: Prioritizing the safe acceleration of AI deployment and usage by securing AI agent identities.
- **Global Expansion**: Deepening presence in key markets like India, the UK, Australia, and across Europe.
- **Platform Growth**: Continuing to unify and enhance the Identity Cloud platform to cover the full spectrum of identity challenges.
- **Talent Acquisition**: Aggressively hiring (+51% job postings YoY) to scale engineering, sales, and customer success teams.

## Why Work Here
- **High-Growth Environment**: Headcount grew 28% in the last year, offering strong career advancement potential in a fast-scaling company.
- **Mission-Driven Work**: Tackling critical cybersecurity challenges for the world’s largest organizations.
- **Global & Remote-First Culture**: Remote-friendly roles and hybrid options available, with offices in major cities globally (El Segundo, London, Sydney, Bengaluru). [saviynt.com](https://saviynt.com/careers)
- **Strong Compensation & Benefits**: Competitive base pay with bonuses, medical/dental/vision, retirement programs, and flexible time off. [saviynt.com](https://saviynt.com/careers)
- **Positive Employee Sentiment**: Average employer rating of **4.0/5.0** (436 reviews), with strong compensation (4.1) and career opportunity (4.0) scores. [linkedin.com](https://linkedin.com/company/saviynt)
- **Diverse, Global Team**: Workforce spans 23 countries, offering a rich, multicultural work experience.

## Sources
1. [LinkedIn Company Profile](https://linkedin.com/company/saviynt)
2. [Saviynt Careers Page](https://saviynt.com/careers)
3. [Saviynt About Us](https://saviynt.com/company/about-saviynt)
4. [Lever Job Postings](https://jobs.lever.co/saviynt)
5. [CB Insights Profile](https://www.cbinsights.com/company/saviynt)

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