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Role at a glance
You will
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…
Experience
13+ years
Must-have skills
Data EngineeringData WarehousingMLOpsMicrosoft AzureRelease Management
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Job description
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: Strong 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
Key skills
Required
Data EngineeringData WarehousingMLOpsMicrosoft AzureRelease Management
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