AI Solution Architect

Signature IT World Inc; Illinois; On-site; USD 150,000 - 190,000; Full time

Job summary

Signature IT World Inc is seeking an experienced AI Solution Architect to design scalable Generative AI and agentic AI solutions for the Asset Management / Investment Management domain.

The role emphasizes Azure, Snowflake, Databricks, Lakehouse architectures, vector databases, and enterprise AI platforms, with collaboration across business, data, and engineering teams to deliver secure, production-ready AI systems.

Qualifications

• Experience as AI Solution Architect with focus on enterprise AI
• Expertise in Generative AI, LLMs and agentic AI architectures
• Proficient in Azure, Snowflake, Databricks and Lakehouse data platforms

Responsibilities

• Design end-to-end AI/ML and Generative AI architectures for enterprise use cases
• Architect Agentic AI solutions including AI agents, RAG pipelines, and knowledge retrieval
• Develop scalable AI solutions using Azure, Snowflake, Databricks, and Lakehouse platforms
• Define AI/ML architecture patterns for data ingestion, model integration, inference, and deployment
• Establish MLOps/LLMOps practices for lifecycle management, deployment, monitoring, and governance
• Ensure integration with enterprise apps, APIs, data platforms, and ecosystems

Skills

AI Solution Architect / AI Architect

Generative AI and LLM

Agentic AI / AI Agents

Azure cloud and AI services

Snowflake

Databricks

Lakehouse architecture

Vector Databases

MLOps / LLMOps

Enterprise AI platforms

AI governance and security

Job description

We are seeking an experienced AI Solution Architect with strong expertise in Generative AI, Agentic AI, cloud architecture, and enterprise data platforms. The ideal candidate will design and implement scalable AI solutions for the Asset Management / Investment Management domain, leveraging Azure, Snowflake, Databricks, Lakehouse architectures, vector databases, and enterprise AI platforms.

The candidate will work closely with business, data, engineering, and architecture teams to translate business requirements into secure, scalable, and production-ready AI solutions.

Key Responsibilities

• Design end-to-end AI/ML and Generative AI architectures for enterprise asset management use cases.
• Architect Agentic AI solutions, AI agents, RAG pipelines, knowledge retrieval, and LLM-based applications.
• Develop scalable AI solutions using Microsoft Azure, Snowflake, Databricks, and Lakehouse platforms.
• Design and implement vector databases, embeddings, knowledge stores, and retrieval architectures.
• Define AI/ML architecture patterns covering data ingestion, model integration, inference, monitoring, and deployment.
• Establish MLOps/LLMOps practices for model lifecycle management, deployment, monitoring, and governance.
• Integrate AI solutions with enterprise applications, APIs, data platforms, and existing technology ecosystems.
• Ensure solutions meet requirements for security, scalability, performance, reliability, and compliance.
• Collaborate with Asset Management stakeholders to understand investment, portfolio, risk, and operational business processes.
• Define responsible AI practices including AI governance, model risk, data privacy, security, and auditability.
• Provide technical leadership and guidance to AI engineers, data engineers, and development teams.
• Evaluate emerging AI technologies and recommend appropriate solutions for enterprise adoption.

Must-Have Skills

• Strong experience as an AI Solution Architect / AI Architect.
• Generative AI and LLM architecture.
• Agentic AI / AI Agents.
• Azure cloud and AI services.
• Snowflake and Databricks.
• Lakehouse architecture and data interoperability.
• Experience with Vector Databases and embeddings.
• MLOps / LLMOps.
• Enterprise AI platforms and API integration.
• Strong understanding of AI governance and security.

Nice-to-Have Skills

• Experience with LLM frameworks and orchestration tools.
• Experience with knowledge graphs and semantic search.
• Experience building enterprise-grade AI copilots and autonomous agents.
• Financial services data, portfolio management, investment research, or risk management experience.
• Experience leading architecture discussions with senior business and technology stakeholders.