Job Summary
AI Context Architect:
Center of Excellence (COE) AI Context Architect AI, Data & Knowledge Architecture
The AI Context Architect makes enterprise knowledge trustworthy and usable by AI systems. The role designs the semantic layer (ontologies, taxonomies and semantic models), architects retrieval on top of it (GraphRAG and hybrid graph-vector patterns across graph databases, vector stores, RDBMS and NoSQL), governs it (metadata, lineage, entity resolution, access and explainability), and exposes it as trusted context to agents and applications through APIs and MCP The role works as a hands-on, Al-native engineer-architect, using agent harnesses in daily delivery, and sets reference patterns that Solutions and Advisory teams reuse with clients.
Key Responsibilities
A. Semantic & Knowledge Modeling
• Define enterprise ontologies, taxonomies, and semantic models that represent business entities, relationships, and rules in a form both people and machines can use. Establish modeling standards, naming conventions, and versioning/change processes for the semantic layer.
• Translate domain and business concepts into graph and relational models with domain
B. Retrieval & Context Architecture Architect GraphRAG and hybrid graph-vector retrieval solutions across Ne04j, Amazon Neptune, vector stores, RDBMS, and NoSQL.
• Design chunking, embedding, indexing, ranking, and query-planning strategies; select the right retrieval pattern per use case (vector, graph traversal, SQL, hybrid).
• Design context assembly for agents: what to retrieve, how to rank, how to cite, and how to fit within context and cost limits.
C. Governance, Trust & Explainability
• Govern metadata, data lineage, and provenance for all context served to AI systems.
• Design and implement entity resolution and master-data alignment across sources. Enforce access control (row/entity/attribute-level, policy-aware retrieval) so AI only sees what the requester is entitled to see. Build explainability into retrieval: traceable sources, reasoning paths, and confidence indicators.
D. Exposure & Integration
• Expose trusted context as secure, versioned APIs and MCP servers consumable by agents, copilots, and applications. Apply cloud-native, microservices, and API design principles for scalable, resilient context services.
• Integrate with enterprise data platforms, catalogs, and identity/security systems.
E. Quality, Operations & Cost
• Define evaluation frameworks for retrieval and context quality (relevance, groundedness, completeness, latency, regression testing).
• Instrument context services for observability (tracing, quality metrics, usage, drift).
• Manage cost and performance trade-offs (index sizes, model calls, query patterns, caching).
F. Engineering Practice & COE Leadership
• Build working reference implementations and accelerators using Al-native engineering practices and agent harnesses. Apply DevSecOps practices: CI/CD, infrastructure as code, automated testing, secure-by- design. Publish reference architectures, patterns, and playbooks; mentor engineers and support Solutions and Advisory teams in client engagements. Evaluate emerging tools, standards, and models, and advise on adoption.
Skill Requirements
Education & Experience
Bachelor's or Master's degree in Computer Science, Information Systems, Data Science, or an equivalent field, or equivalent practical experience.
10+ years in software, data, or solution architecture, with at least 3 years in knowledge graphs, semantic modeling, search/retrieval, or applied AI/LLM systems. Track record of taking at least one graph- or retrieval-based AI solution into production.
Technical Skills (Must Have)
1. Semantic modeling: ontologies, taxonomies, semantic models, knowledge representation; property graph modeling.
2. Graph technologies: hands-on with Ne04jand/orAmazonNeptune;Cypher, Gremlin, or SPARQL.
3. Retrieval architecture: GraphRAG and hybrid graph-vector retrieval; embeddings, vector stores, re-ranking, query planning.
4. Polyglotdata:RDBMS (SQLdesignand optimization) and NoSQL (document, key-value, wide-column) and how to combine them in retrieval.
5. Governance: metadata management, lineage, entity resolution, access control, and explainability for AI.
6. Context exposure: designing and building APIsand MCP servers for agent consumption.
7. Al-native engineering: practical useofAIin the development lifecycle; hands-on with at least one agent harness (Claude Code, GitHub Copilot, Codex, Kiro, or Cursor).
8. Modern application development: strongproficiencyin at least one language such as Python, TypeScript/Node, or Java; clean architecture, testing, code review.
9. Cloud-native engineering: AWS/Azure/GCP services, containers, microservices, API design, event-driven patterns.
IO. DevSecOps: CI/CD, IaC, secrets management, secure SDLC, dependency and supply- chain hygiene.
11. Enterprise Alhygiene: security, governance, evaluation, observability, and cost awareness for LLM- based systems. Behavioral & Professional Skills Structured, first-principles thinking; able to turn ambiguous business domains into clean models. Clear communication with executives, domain experts, and engineers; strong written architecture documentation. Consultative mindset: balances elegance with delivery pragmatism. Ownership, mentoring, and a collaborative approach in a COE setting
Other Requirements
Role- specific • Semantic web SHACL, SKOS,JSON-LD. Graph algorithms and graph data science (community detection, centrality, embeddings). LLM-assisted knowledge graph construction (entity/relation extraction, ontology induction). Data catalog and governance platforms (e.g„ Collibra, Alation, DataHub, OpenMetadata). Evaluation tooling for RAG/agents (e.g., RAGAS, TruLens, LangSmith, custom harnesses). Orchestration and agent frameworks (LangGraph, Llamalndex, Semantic Kernel, custom agents). Search technologies (OpenSearch/E1asticsearch, BM25 + vector hybrid search). Streaming and data pipelines (Kafka, Spark, dbt, Airflow). Domain exposure in BFSI, healthcare/life sciences, manufacturing, retail, or public Common competencies (mandatory across all roles) • Practical Al-native engineering and strong modern application development. • Cloud-native, API, microservices, and DevSecOps grounding. Hands-on experience with at least one agent harness (Claude Code, GitHub Copilot, Codex, Kiro, Cursor). Enterprise security, governance, evaluation, observability, and cost awareness.
Certifications (preferred, not mandatory) Cloud architect certifications (AWS, Azure, or GCP). Ne04j Certified Professional / Graph Data Science certification. TOGAE DAMA CDMP, or equivalent architecture/data management certification