Principal Software Engineer, AI Platform Engineering

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

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

8+ years of data engineering at production scale across multiple companies

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