Data & AI Architect

Description

We are looking for a Data & AI Architect to lead the design of modern data platforms and intelligent systems.. This is a fully remote opportunity that blends enterprise data architecture with applied AI, focusing on scalable platforms, retrieval-driven solutions, and production-grade agentic workflows. The ideal candidate will shape technical standards, guide platform decisions, and contribute hands-on expertise to build secure, high-performing solutions that support advanced analytics and AI adoption.

Responsibilities:

Establish enterprise data models, shared business semantics, and data interface standards that support secure, tenant-aware access across relational, document, key-value, object, graph, vector, and analytical data platforms.

Design cloud-based data ecosystems, including warehouses and lakehouses, and select the right technologies based on throughput, latency, scale, and cost requirements.

Create architecture for batch and streaming pipelines, transformation layers, and data-serving systems with strong attention to lineage, recoverability, freshness, data quality, and operational accountability.

Lead the design of embedding pipelines, vector lifecycle management, semantic retrieval, hybrid search, reranking strategies, and evaluation methods for high-quality search experiences.

Architect knowledge-driven and graph-aware solutions that improve entity resolution, recommendation quality, matching accuracy, and personalization outcomes.

Define production-ready approaches for retrieval-augmented AI and single- or multi-agent systems, including context design, citation grounding, orchestration patterns, state handling, approval workflows, and resilient failure recovery.

Establish measurement and observability practices for AI systems by tracking quality, safety, reliability, cost, latency, and task performance through traceable debugging and regression testing.

Build, review, and optimize critical data pipelines, AI prototypes, retrieval services, and evaluation frameworks to support real-world production use cases.

Implement repeatable engineering practices such as automated testing, CI/CD, documentation, monitoring, and recovery procedures that enable reliable long-term operation.

Set architectural direction for governance, privacy, authorization, retention, auditability, and AI safety controls while advising on platform strategy, vendor evaluation, and technical decision-making.

Requirements

Proven experience designing enterprise data architecture across modern cloud and hybrid data platforms.
Strong expertise with AI solution architecture, including retrieval-based systems, vector search, and agentic AI patterns.
Hands-on knowledge of technologies such as Snowflake, NoSQL platforms, OLAP systems, and AWS-based data services.
Demonstrated ability to build scalable, secure architectures with strong data governance, privacy, and access control principles.
Experience implementing CI/CD, testing, observability, and operational readiness practices for data and AI solutions.
Ability to evaluate technical tradeoffs and recommend tools or platforms based on performance, maintainability, and business value.
Strong communication and leadership skills with experience mentoring engineers and presenting architectural guidance clearly.
Background in architecture design for complex data services, analytical platforms, and production-grade AI applications.

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