Lead Data Engineer, AI

🕒 June 12

🇮🇳 India – Remote

⏰ Full Time

🟠 Senior

🚰 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

• Build, test, and maintain production pipelines (batch & real-time) on Snowflake, PySpark, Delta Lake, and Kafka. • Implement data quality checks, schema validation, and alerting at every pipeline stage. • Migrate legacy ETL/DWH to cloud-native AWS/Azure architectures with measurable latency and cost improvements. • Maintain CI/CD pipelines: automated testing, deployment, rollback, and IaC (Terraform, GitHub Actions). • Build end-to-end retrieval infrastructure: document ingestion, embedding pipelines, vector store management (Pinecone, FAISS, ChromaDB, OpenSearch), and hybrid retrieval layers. • Implement chunking, metadata filtering, and re ranking — tuning for precision, recall, and latency. • Maintain data freshness and index consistency; instrument with context relevance and faithfulness metrics. • Implement and maintain business entity mappings, ontologies, and knowledge graphs (Neo4j) per Architect design. • Build and version the feature store and semantic data contracts serving both ML models and LLM applications. • Manage metadata, data lineage, and audit trail instrumentation across the platform. • Build ML data infrastructure: training curation, feature engineering, MLflow experiment tracking, dataset versioning. • Support LLM fine-tuning workflows — corpus curation, quality filtering, dataset formatting. • Implement automated evaluation pipelines: factual accuracy, hallucination detection, regression tracking. • Maintain production monitoring dashboards for pipeline health, model metrics, and alerting. • Build and maintain data APIs, tool schemas, and memory/state stores that autonomous agents depend on. • Implement agent observability: capture inputs, retrieved context, tool calls, reasoning traces, and outputs. • Maintain text-to-SQL layers, semantic query interfaces, and context APIs for conversational AI consumers. • Implement RBAC, attribute-based access, PII detection/masking, data classification, and audit logging. • Enforce data contracts and schema governance with automated breaking-change detection and versioned migrations. • Build data quality monitoring (completeness, freshness, consistency) with automated alerting and root-cause tooling. • Support compliance readiness: audit trails, data provenance, and regulatory documentation.

🎯 Requirements

• 7+ years data engineering using Cloud services • 2+ years production AI/ML or LLM-era data infrastructure. Proven experience building production pipelines at scale — batch and streaming, Snowflake,AWS/Azure. • Deep expertise: Python, PySpark, Snowflake, Delta Lake, Kafka, Spark Structured Streaming. • Hands-on with vector stores, embedding pipelines, and retrieval infrastructure in production RAG environments. • Working knowledge of MLOps: MLflow, CI/CD for AI, automated evaluation, and production monitoring. • Strong grounding in data governance, quality frameworks, and compliance-**aligned engineering.

🏖️ Benefits

• Health insurance • 401(k) matching • Flexible work hours • Paid time off • Remote work options