Data Architect - AI & Knowledge Platform

Weblife Stores; United States; Remote; USD 180,000 - 250,000; Full time

Benefits offered by this job

Founding ownership of data and memory

Flexible remote work

Global, remote-friendly collaboration

Job summary

WebLife Stores is seeking a founding Data Architect to design and build the data and knowledge systems at the core of an AI‑native platform. You will own application data end‑to‑end across cloud, local, and offline environments, defining governance, security, and scalable architectures.

You will shape knowledge structures, embeddings, and RAG pipelines, while leading by example within a small founding team. This remote role welcomes candidates worldwide.

Qualifications

• Bachelor's degree in Computer Science, Software Engineering, Data Engineering, or a related field. A master's degree is preferred.
• 57+ years of experience in Data Engineering, Platform Engineering, Backend Engineering, or related technical roles.
• 3+ years of experience owning data architecture or platforms end-to-end.
• Experience designing application databases beyond analytics or reporting systems.
• Experience working in startup or high‑ownership environments is highly desirable.
• Experience working with international teams or clients is advantageous.

Responsibilities

• Own the end-to-end architecture of application data across cloud, local, and offline‑capable environments.
• Define database governance standards, including schemas, primary keys, migrations, backward compatibility, and change management.
• Design secure multi‑tenant data models, tenant isolation strategies, and data lifecycle management.
• Architect for extensibility across relational, vector, graph, search, and future data stores.
• Design and implement organizational memory systems, including knowledge records, semantic chunks, embeddings, metadata, relationships, and audit history.
• Build and operate RAG pipelines covering ingestion, chunking, embedding, indexing, retrieval, ranking, and evaluation.
• Design knowledge structures such as context graphs, hierarchies, and retrieval models that improve AI reasoning.
• Establish governance mechanisms for knowledge freshness, approval workflows, lifecycle states, and auditability.
• Translate research‑grade AI memory and retrieval concepts into pragmatic MVPs while avoiding over‑engineering and preserving long‑term architectural flexibility.
• Build reliable event‑driven and batch processing pipelines with strong guarantees around data quality, idempotency, and scalability.
• Own infrastructure, CI/CD, observability, monitoring, and production reliability for data services.
• Troubleshoot production issues, perform root‑cause analysis, and drive continuous improvement.
• Partner with AI engineers, product teams, and stakeholders to translate ambiguous requirements into scalable production systems.
• Balance long‑term architectural decisions with pragmatic MVP delivery.
• Mentor engineers and establish best practices as the platform scales.

Skills

PostgreSQL

SQLite

pgvector

RAG pipelines

Embeddings

Vector databases

Multi-tenant architectures

CI/CD

Education

Bachelor's degree in CS/SE

Master's degree preferred

Tools

Qdrant

Pinecone

Weaviate

Neo4j

Job description

Data Architect AI & Knowledge Platform

ABOUT WEBLIFE

For over 16 years, WebLife has been a leader in the e-commerce industry, operating category-defining stores and becoming one of the largest distributors of mailboxes in the U.S. We have a long history of building data-driven operations and best-in-class teams.

We are now building an AI-native operating system for how small and mid-sized businesses run their operations. Our product transforms AI from one-shot chat into a durable, persistent system with real institutional memory.

This is a small, high-energy founding team that combines the creative speed of a startup with the stability and resources of an established company.

ABOUT THE ROLE

As our founding Data Architect, you will design and build the data and knowledge systems at the core of our AI-native platform. This is a hands‑on architecture role where you will own application data end-to-end, including modeling, storage, synchronization, governance, and production operations.

Our platform combines traditional application data with AI-native memory systems, including semantic retrieval, RAG pipelines, embeddings, context graphs, and knowledge structures that enable AI to reason over organizational memory.

You will define the data foundations for an offline‑capable, cloud‑synced product, ensuring scalability, security, auditability, and long-term flexibility across relational, vector, graph, and future data stores.

We invest heavily in getting the data model and system boundaries right, then execute rapidly. As part of our small founding team, you will lead by doing, setting technical direction while actively building systems that reach customers.

This is a remote role open to candidates based in Sri Lanka.

KEY RESPONSIBILITIES

1. Data Architecture and Governance

• Own the end-to-end architecture of application data across cloud, local, and offline‑capable environments.
• Define database governance standards, including schemas, primary keys, migrations, backward compatibility, and change management.
• Design secure multi‑tenant data models, tenant isolation strategies, and data lifecycle management.
• Architect for extensibility across relational, vector, graph, search, and future data stores.

2. Knowledge Systems and AI Memory

• Design and implement organizational memory systems, including knowledge records, semantic chunks, embeddings, metadata, relationships, and audit history.
• Build and operate RAG pipelines covering ingestion, chunking, embedding, indexing, retrieval, ranking, and evaluation.
• Design knowledge structures such as context graphs, hierarchies, and retrieval models that improve AI reasoning.
• Establish governance mechanisms for knowledge freshness, approval workflows, lifecycle states, and auditability.
• Translate research‑grade AI memory and retrieval concepts into pragmatic MVPs while avoiding over‑engineering and preserving long‑term architectural flexibility.

3. Data Platforms and Operations

• Build reliable event‑driven and batch processing pipelines with strong guarantees around data quality, idempotency, and scalability.
• Own infrastructure, CI/CD, observability, monitoring, and production reliability for data services.
• Troubleshoot production issues, perform root‑cause analysis, and drive continuous improvement.

4.. Collaboration and Leadership

• Partner with AI engineers, product teams, and stakeholders to translate ambiguous requirements into scalable production systems.
• Balance long‑term architectural decisions with pragmatic MVP delivery.
• Mentor engineers and establish best practices as the platform scales.

REQUIREMENTS

1. Education and Experience

• Bachelor's degree in Computer Science, Software Engineering, Data Engineering, or a related field. A master's degree is preferred.
• 57+ years of experience in Data Engineering, Platform Engineering, Backend Engineering, or related technical roles.
• 3+ years of experience owning data architecture or platforms end-to-end.
• Experience designing application databases beyond analytics or reporting systems.
• Experience working in startup or high‑ownership environments is highly desirable.
• Experience working with international teams or clients is advantageous.

2. Data and Platform Expertise

• Strong expertise in PostgreSQL, including schema design, indexing, migrations, performance tuning, and operational reliability.
• Experience with offline‑first systems, synchronization patterns, SQLite, or client‑cloud architectures.
• Strong understanding of data ownership, schema governance, backward compatibility, and database change management.
• Experience designing multi‑tenant SaaS platforms with tenant isolation and security controls.
• Familiarity with infrastructure as code, CI/CD, observability, and cloud‑native architectures.

3. AI and Knowledge Systems

• Hands‑on experience building RAG pipelines and retrieval systems in production.
• Experience with embeddings, semantic search, and vector databases such as Qdrant, Pinecone, Weaviate, pgvector, or similar technologies.
• Familiarity with graph databases or structured memory systems such as Neo4j or similar technologies.
• Understanding of LLM fundamentals, including tokens, embeddings, context windows, retrieval, and grounding.
• Experience with workflow orchestration frameworks such as LangGraph or similar tools.
• Ability to design evaluation approaches for retrieval relevance, answer quality, citation accuracy, and knowledge freshness.
• Interest in learning and researching emerging literature and frameworks related to contextual memory, cognitive architectures, organizational memory, agent memory, and structured knowledge systems.

TECHNOLOGY STACK

• Data and Storage: PostgreSQL, SQLite, pgvector
• Vector Search: Qdrant, Pinecone, Weaviate
• Graph Systems: Neo4j and graph‑based knowledge systems
• AI and Retrieval: OpenAI, embeddings, RAG, semantic search
• Orchestration: LangGraph and event‑driven workflows
• Infrastructure: Terraform, cloud‑native services, CI/CD
• Observability: Monitoring, logging, alerting, and data quality tools
• Security: Multi‑tenant architectures, IAM, encryption, and auditability

Our technology landscape is evolving, and we value strong fundamentals and sound judgment over familiarity with specific tools.

BENEFITS

• Competitive USD-based compensation.
• Founding ownership of the application data, organizational memory, and knowledge systems at the core of an AI‑native product.
• Cutting‑edge, AI‑driven work involving RAG, knowledge structures, vector and graph systems, agent memory patterns, and a modern cloud stack.
• A culture that encourages curiosity and lifelong learning.
• Clear career paths and personal development opportunities.
• A flexible work‑from‑home setup as part of a globally connected team.