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.

Solid 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:

- End-to-End Solution Architecture

2.

Architecture Decision

Records (ADRs)

- High-Level Architecture (HLD)
- Low-Level Architecture (LLD)
- Semantic/Knowledge Graph Architecture
- AI/LLM Architecture

7.

Integration

Architecture

8.

Security

Architecture

- API Architecture
- Data Flow &
- Sequence Diagrams

11.

Deployment

Architecture

12.

Technology

Selection &

- Trade-off Analysis
- 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.

📌 Senior Solutions Architect (Coimbatore)
🏢 Vimix Technologies
📍 Coimbatore