全职 Senior Data Engineer-Data Engineer 工作, 薪水 up to SGD 12,000, APBA TG HUMAN RESOURCE PTE. LTD. Islandwide (Singapore) 公司招聘中 - Ricebowl

Job Description

This role sits within NCS AI Central's (AIC) Forward Deployed Engineering (FDE) model — the combined capability that takes AI solutions from proof-of-concept through to hardened production systems. You will operate across both fast-moving FDE engagements (POC/POV, pilot deployments for strategic and lighthouse clients) and steady-state system development and maintenance work — bringing the same rigor and a reusable, asset-fed approach to both.

What will you do:

1. Data Pipeline Engineering & AI-Readiness

•      Design and build ingestion, cleaning, and transformation pipelines that turn messy, real-world client data into AI-ready datasets.

•      Build batch and streaming pipelines (Airflow/Prefect/Kafka) that keep data flowing reliably into AI systems without manual intervention.

•      Own data quality — deduplication, schema validation, completeness checks — upstream of any model or RAG pipeline.

•      Proactively flag data gaps or quality issues that would degrade model/RAG performance downstream, before they surface as an AI Engineer's problem in testing.

2. RAG & Vector Store Architecture

•      Architect document/data ingestion and indexing pipelines for Retrieval-Augmented Generation (RAG) systems — chunking strategy, embeddings, hybrid/vector search.

•      Design and operate vector database and search infrastructure (pgvector/Pinecone/OpenSearch) at production scale and query volume.

3. Data Governance & Compliance

•      Implement PII redaction, data residency, and access-control patterns aligned to PDPA and sector-specific requirements (Healthcare, Government, Transport).

•      Maintain clear data lineage and metadata governance so engagement teams and auditors can trace how client data flows into AI outputs.

4. FDE & Development/Maintenance Coverage

•      During FDE engagements: rapidly assess and prepare a client's data landscape during Discover/POC, identifying data-readiness gaps early.

•      During system development & maintenance engagements: build and operate production-scale data pipelines handling the full volume and complexity of live client systems (e.g., Healthcare or Transport data at scale).

•      Contribute reusable ingestion/indexing patterns back into the shared internal asset library to accelerate future engagements.

5. Collaboration & Leadership

•      Partner closely and continuously with AI Engineers and AI Architects — understanding what a given model, RAG pipeline, or agent actually needs from the data layer, and translating that into concrete pipeline and schema design decisions.

•      Own the definition of "AI-ready" data for each engagement jointly with AI Engineers — agreeing on chunking strategy, metadata, freshness, and quality thresholds before pipelines are built, not after retrieval quality suffers.

•      Sit in solution design conversations alongside AI Engineers and AI Architects, so data architecture and model/RAG architecture are designed together rather than data being treated as a downstream dependency.

•      Mentor junior data engineers and set data engineering standards across engagements.

Qualifications

The ideal candidate should possess:

•      10+ years in data engineering, including production-scale pipeline design (not just analytics/reporting pipelines).

•      Strong SQL and at least one systems language (Python/Scala/Java); hands-on with batch and streaming frameworks (Airflow, Spark, Kafka).

•      Experience building data pipelines for AI/ML or RAG use cases — embeddings, vector indexing, hybrid search.

•      Solid understanding of data governance, PII handling, and access-control patterns in regulated environments.

•      Comfortable moving between fast, exploratory data assessment (FDE/POC) and disciplined, high-volume production pipeline engineering (system development & maintenance).

•      Working understanding of core AI/LLM concepts — tokenization, embeddings, chunking strategy, context windows, RAG, and agentic workflows — sufficient to hold a real technical conversation with AI Engineers and AI Architects about what "AI-ready" data means for a given use case, not just how to move and clean it.

Preferred Qualifications

•      Experience with vector databases (pgvector, Pinecone, Weaviate) and search platforms (OpenSearch/Azure AI Search).

•      Exposure to Singapore Government data environments (GCC/HCC) and compliance regimes (IM8, PDPA).

•      Experience with sector-specific data complexity — Healthcare (clinical data governance) or Transport/Aviation systems.

•      Familiarity with data cataloguing and lineage tooling.

•      Prior experience embedded within an AI/ML delivery team (not just a data platform team) — i.e., has sat alongside AI Engineers day-to-day and adjusted pipeline/schema design based on model or RAG performance feedback.

Tech Stack (Illustrative)

•      Languages: Python, SQL (Scala/Java a plus)

•      Pipelines: Airflow/Prefect, Spark, Kafka/Debezium

•      Storage/Search: Postgres, S3/Blob, pgvector/Pinecone/Weaviate, OpenSearch/Azure AI Search

•      Governance: Presidio (PII redaction), data catalogue/lineage tooling

•      Cloud: AWS/Azure/GCP; GCC/HCC exposure a plus