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 — a product that turns 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—from modeling, storage, synchronization, and governance through to 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. In 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
Data Architecture & 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.
Knowledge Systems & AI Memory
- Design and implement organizational memory systems including knowledge records, semantic chunks, embeddings, metadata, relationships, and audit history.
- Build and operate RAG pipelines including 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, avoiding over-engineering while preserving long-term architectural flexibility.
Data Platforms & Operations
- Build reliable event-driven and batch processing pipelines with strong guarantees around 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.
Collaboration & 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
Education & Experience
- Bachelor's degree in Computer Science, Software Engineering, Data Engineering, or a related field; Master's preferred.
- 5–7+ years of experience in Data Engineering, Platform Engineering, Backend Engineering, or related technical roles.
- 3+ years 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.
Data & 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.
AI & 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.
- 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 tooling.
- Ability to design evaluation approaches for retrieval relevance, answer quality, citation accuracy, and knowledge freshness.
- Interest in learning and researching emerging literature and frameworks around contextual memory, cognitive architectures, organizational memory, agent memory, or structured knowledge systems.
Technology Stack
- Data & Storage: PostgreSQL, SQLite, pgvector
- Vector Search: Qdrant, Pinecone, Weaviate
- Graph Systems: Neo4j and graph-based knowledge systems
- AI & 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 tooling
- Security: Multi-tenant architectures, IAM, encryption, and auditability
Our technology landscape is evolving, and we value fundamentals & judgement over 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.
- Bleeding-edge, AI-driven work — 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.
