Senior Data Engineer AI

You will operate across both fast-moving Forward Deployed Engineering (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.

• 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.

• 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.

• 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.

• 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.

• 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.

• 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 areal 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.

• 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.

• 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