Vimix Technologies LLP
Website:
vimix.app
Job details:
Role: Solution Architect – Semantic Governance AI Platform
Engagement: Project / Contract / Strategic Collaboration
Experience: 8+ years preferred
Domain: Enterprise Data Governance | AI/ML | Semantic Architecture | Knowledge Graphs
Location: Remote / Hybrid
About the Project
We are building an enterprise Semantic Governance AI Platform that establishes authoritative business definitions, manages semantic conflicts, maintains regulatory and decision provenance, and ensures approved definitions are consistently implemented across data warehouses, BI platforms, ML systems and enterprise applications.
The platform will integrate with technologies such as Snowflake, Databricks, Power BI, Tableau, Collibra/Alation and enterprise data platforms.
Role Overview
We are looking for an experienced Solution Architect who can own the end-to-end technical architecture and work closely with business stakeholders, Data Governance teams, Data Scientists, Data Engineers and development teams.
The architect will translate business governance requirements into a scalable architecture covering semantic modeling, workflow, AI, knowledge graphs, integrations, security, APIs and enterprise data platforms.
Key Responsibilities
Architecture
* Design the end-to-end architecture for the Semantic Governance Platform.
* Define application, data, integration, semantic and AI architecture.
* Design the canonical semantic registry and concept/version model.
* Define architecture for ontology and knowledge-graph components.
* Design workflow architecture for proposal, review, approval, escalation and deprecation.
* Define APIs and integration patterns for enterprise platforms.
Data & Semantic Architecture
* Design semantic models for business concepts, relationships, formulas and dependencies.
* Define metadata, lineage and provenance architecture.
* Design mechanisms for identifying conflicting definitions.
* Define semantic impact-analysis architecture.
* Establish standards for canonical definitions and version management.
AI Architecture
* Design AI/LLM components for:
* Semantic similarity
* Concept matching
* Conflict detection
* Regulatory document analysis
* Impact analysis
* AI-assisted governance
* Define appropriate use of LLMs, embeddings, vector databases, RAG and knowledge graphs.
* Establish human-in-the-loop controls so AI recommendations do not automatically become authoritative definitions.
* Define evaluation, explainability and guardrail mechanisms.
Enterprise Integration
Design integration patterns with:
* Snowflake
* Databricks
* Power BI
* Tableau
* Collibra / Alation
* Data catalogs
* Feature stores
* Enterprise APIs
* Existing Claims, Underwriting and Finance systems
Security & Compliance
* Design RBAC and fine-grained access controls.
* Define authentication/authorization architecture.
* Design immutable/tamper-evident audit architecture.
* Address encryption, secrets management and data protection.
* Ensure architecture supports enterprise security and compliance requirements.
Technical Leadership
* Create HLD and LLD architecture documentation.
* Produce architecture diagrams using Draw.io, Lucidchart or Mermaid.
* Create API, integration and data-flow specifications.
* Participate in technical discussions with client stakeholders.
* Guide engineering teams during implementation.
* Conduct architecture reviews and identify technical risks.
* Make technology and architectural trade-off decisions.
Required Technical Skills
Must Have:
* 8+ years of enterprise solution architecture experience.
* Strong experience with data platforms and enterprise integration.
* Strong understanding of Data Governance and Metadata Management.
* Experience with cloud architecture — AWS, Azure or GCP.
* Strong SQL and data architecture knowledge.
* REST APIs and event-driven architecture.
* Experience with Snowflake and/or Databricks.
* Experience integrating BI platforms such as Power BI/Tableau.
* Understanding of ontology, semantic models or knowledge graphs.
* Strong understanding of AI/ML architecture and LLM-based applications.
* Experience with security, RBAC and enterprise architecture patterns.
Good to Have:
* Neo4j / Amazon Neptune / other graph databases.
* RAG and vector databases.
* Python.
* LangChain / LlamaIndex or equivalent frameworks.
* Collibra / Alation.
* Microsoft Purview.
* Data lineage platforms.
* Regulatory/compliance technology.
* Insurance, Banking, Financial Services or Risk domain experience.
Expected Deliverables
The Solution Architect will be responsible for producing:
1. End-to-End Solution Architecture
2. Architecture Decision Records (ADRs)
3. High-Level Architecture (HLD)
4. Low-Level Architecture (LLD)
5. Semantic/Knowledge Graph Architecture
6. AI/LLM Architecture
7. Integration Architecture
8. Security Architecture
9. API Architecture
10. Data Flow & Sequence Diagrams
11. Deployment Architecture
12. Technology Selection & Trade-off Analysis
13. POC Architecture
14. Implementation Roadmap
15. Technical Standards for Engineering Teams
Key Question We Expect the Architect to Solve
The architect should be able to answer:
How do we take an approved business definition such as “Loss Ratio”, represent it as a canonical semantic object, map it to regulatory requirements, detect conflicting implementations in Snowflake/Power BI/ML systems, perform impact analysis when the definition changes, and propagate the approved version across the enterprise — while keeping humans accountable for the final decision?
Ideal Candidate
We are looking for someone who can operate at both levels:
Business → Architecture → Engineering
and can confidently sit with:
CDO / CFO / CRO → Data Governance → Enterprise Architects → Data Scientists → Data Engineers → Developers
and convert the business requirement into an executable technical architecture.
Click on Apply to know more.
