Key Responsibilities & Work Expectations

1. End-to-End Solution Architecture

- Design and architect end-to-end Generative AI and Agentic AI solutions tailored to complex business use cases.
- Take full ownership of the lifecycle: driving solutions from discovery, architecture, and development, through to successful production rollout.
- Work closely with business and engineering stakeholders to translate requirements into scalable architecture designs.

2. Data Platform & Cloud Integration

- Design robust data engineering pipelines to extract, curate, and prepare enterprise data for consumption by LLMs and AI agents.
- Implement solutions using core Azure data services (e.g., Cosmos DB, Azure Cognitive Search/Azure AI Search, Azure Blob Storage).
- Architect data retrieval mechanisms from enterprise warehouses (primarily Snowflake) for heavy-data GenAI use cases.

3. Production Rollout & Operations Management

- Oversee production rollouts and cutover planning, ensuring smooth transitions from development to live environments.
- Define and implement Non-Functional Requirements (NFRs) including observability, monitoring, performance scaling, and fault tolerance for Day 0 / Day 1 operations.




- Collaborate directly with operations and support teams to establish operational readiness before handing over deployed solutions.

Key Qualifications & Technical Requirements

- Total Experience: 13+ years in IT/Data Architecture, with proven experience building enterprise-grade systems.
- Core Background: Solid foundation in Data Engineering & Data Solution Architecture, transitioning into Generative AI over recent years.
- GenAI & Agentic AI Expertise: Hands-on experience architecting GenAI systems, RAG (Retrieval-Augmented Generation) frameworks, custom agentic workflows, and LLM orchestration.
- Cloud Infrastructure: Hands-on expertise in Azure Cloud Services (Azure AI Search, Cosmos DB, Blob Storage, etc.).
- Data Warehousing: Solid experience working with Snowflake as a primary curated analytics data source.
- DevOps & MLOps/LLMOps: Direct experience with production release management, cutover strategy, deployment pipelines, and operational readiness.

Interview & Hiring Process Context

- Hiring Manager / L1 Interview: Focus on overall hands-on architecture, GenAI end-to-end delivery experience, and rollout capabilities
- L2 Technical Evaluation: Data Architect - Focus on data pipelines, Snowflake integration, Azure infrastructure, and governance controls

📌 Senior GenAI Solution Architect (Bengaluru)
🏢 TRIGENT SOFTWARE PRIVATE
📍 Bengaluru