AI Data Architect

1 мес. назад

USARemote

data architecturemachine learningaidata governancesecurity

AI Data Architect to design and govern AI data platforms powering enterprise AI initiatives.

3Pillar is an AI transformation partner on a mission to help enterprises build the AI-native products and intelligent agents that will define the next era of business. With teams across North America, Europe, Latin America, and Asia, we work with the most ambitious companies in financial services, healthcare, media, and technology — helping them move faster, modernize boldly, and compete on their own terms. Our HelixAI platform and Helix Pods delivery model put our engineers at the center of real agentic transformation — doing work that is open, portable, and built to last. We are building the future of enterprise AI.   AI Data Architect   We are looking for an AI Data Architect to design, build, govern, and evolve the single source of truth that powers every AI initiative in our

15+ years of hands-on data engineering and architecture experience, alongside building production AI/ML and LLM-era data infrastructure.
Strong Experience with either Databricks or Snowflake; experience with both is desirable.
Strong data architecture patterns & principles, ability to design secure & scalable data lakes, data warehouse, data hubs, and other event-driven architectures
Expertise in designing and writing ETL processes in Python / Java / Scala
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.
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.
Proven experience designing enterprise-scale AI data platforms that serve multiple AI consumers —not just one application or pipeline.
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.

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. Strong exposure to different Data architectures, data lake & data warehouse Define tools & technologies to develop automated data pipelines, write ETL processes, develop dashboard & report and create insights

RAG, Vector & Retrieval Infrastructure Design the retrieval infrastructure that powers RAG-based AI applications: embedding pipelines, vector stores (Pinecone, FAISS, ChromaDB, OpenSearch), chunking strategies, and hybrid retrieval layers combining semantic search with structured queries.   Agentic Behaviour Observability & Output Accuracy Own the observability stack for AI agent behaviour: instrument agents to capture inputs, retrieved context, tool calls, reasoning traces, and outputs — creating a complete audit trail of every agentic action driven by platform data. Design and operate evaluation frameworks that continuously measure AI output quality: factual accuracy, context faithfulness, retrieval relevance, hallucination rates, and task completion success— across all AI c