Team Lead - Data & AI

Key ResponsibilitiesData Architecture & GovernanceOwn 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 MemoryDesign 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 & OperationsBuild 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 & LeadershipPartner 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.RequirementsEducation & ExperienceBachelor'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 ExpertiseStrong 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 SystemsHands-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.