Senior Data Engineer – AI

Аналитика рынка

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Бесплатно, после входа в кабинет.

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Описание

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. Responsibilities 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. 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. 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

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