Cloud Data Architect, AI Experience

🕒 June 12

🇮🇳 India – Remote

⏰ Full Time

🟠 Senior

🔴 Lead

🚰 Data Engineer

👻 Ghost score 32%

What it means: how likely this posting is sitting open without active hiring.How to read it: 60% or higher means we think this is a ghost. Lower scores are less of a worry.How we score it: age, reposts, open roles vs team size, how fast jobs close, and whether pay is listed.Fine print: an estimate from public data, not a judgment of the company. Real jobs can score high, so treat it as a hint, not a fact.

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3Pillar Global

WebsiteLinkedInAll Job Openings

1001 - 5000 employees

💼 Consulting

🏥 Healthcare

🛡️ Insurance

💰 Private Equity Round on 2021-10

Consulting • Healthcare • Insurance

3Pillar Global is a modern application strategy, design, and engineering firm that specializes in delivering strategic software development initiatives for various industries. They offer a range of services, including application technology strategy, digital product engineering, data and analytics, and artificial intelligence development. 3Pillar Global focuses on helping organizations transform their bold ideas into breakthrough solutions by leveraging cutting-edge technologies such as generative and multimodal AI. They work with partners and clients across multiple sectors, including healthcare, financial services, insurance, media, and information services, to solve complex technology challenges and deliver high-performing results.

📋 Description

• Architect and own the enterprise AI data platform — the unified, governed layer that ingests, transforms, stores, and serves all data consumed by AI systems across the organisation. • Design multi-domain data models (lakehouse, data mesh, event-driven) that are structured from day one to serve AI workloads: clean lineage, versioned schemas, well-documented contracts, and low-latency serving APIs. • Own the full data stack: real-time streaming (Kafka, Spark Structured Streaming), batch processing (Databricks, PySpark, Delta Lake), cloud storage and compute (AWS, Azure), and data quality /metadata management. • Ensure this platform is the single, authoritative data source for all downstream consumers — conversational AI, dashboard assistants, autonomous agents, ML models, and reporting — eliminating data silos and conflicting truths. • Drive modernisation of legacy pipelines (on-prem ETL, batch DWH) to cloud-native, AI-ready architectures with measurable improvements in cost, latency, and delivery velocity. • Design the semantic layer that sits above raw data — business-aligned ontologies, entity relationships, domain taxonomies, and knowledge graphs — so AI systems understand context, not just tokens. • Build and maintain knowledge graphs (Neo4j or equivalent) that capture relationships between business entities, policies, KPIs, hierarchies, and domain rules — enabling structured reasoning alongside unstructured retrieval. • Define and govern a feature store and semantic data contracts that serve both classical ML models and LLM-based applications from a single, well-versioned, trusted source. • Own metadata management, data lineage, and audit trails across the semantic layer — ensuring every AI system can trace its outputs back to source data with full accountability. • Design and enforce a comprehensive data governance model that governs access for both human users and AI agents — with role-based access control (RBAC), attribute-based policies, and agent-specific permission scopes that prevent privilege escalation.

🎯 Requirements

• 15+ years of hands-on data engineering and architecture experience, with 3–5+ years building production AI/ML and LLM-era data infrastructure. • Proven experience designing enterprise-scale AI data platforms that serve multiple AI consumers — not just one application or pipeline. • Deep expertise in lakehouse and data mesh architectures: Databricks, Delta Lake, PySpark, Kafka, Spark Structured Streaming, cloud-native data services (AWS, Azure). • Hands-on experience with vector stores, semantic models, knowledge graphs, and retrieval infrastructure in production environments. • Working knowledge of LLMOps: model serving pipelines, MLflow, CI/CD for AI, automated evaluation, and production monitoring. • Strong background in data governance, security, and compliance in regulated industries (financial services, payments, cybersecurity, healthcare). • Experience defining data access controls for AI agents and automated systems — not just human users.

🏖️ Benefits

• Health insurance • Flexible work hours • Professional development opportunities