Principal Software Engineer, AI Platform Engineering

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About the role

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 contr

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