Solutions Architect(Enterprise AI)

Role Overview

We are looking for an Solutions Architect - Enterprise AI to design and deliver enterprise AI and digital transformation solutions for corporate clients.

The role involves understanding business requirements, designing AI solution architectures, integrating AI with existing enterprise systems, and providing technical guidance throughout project implementation.

Key Responsibilities

• Understand clients’ business and technical requirements.

• Design enterprise AI solutions, including AI assistants, RAG systems, AI agents and workflow automation.

• Design system architecture, APIs, databases, cloud infrastructure and integration solutions.

• Integrate AI applications with clients’ existing business systems.

• Select suitable AI models, cloud services and data technologies.

• Define security, access control, data protection and AI governance requirements.

• Prepare architecture diagrams, technical proposals and implementation plans.

• Provide technical guidance to development teams.

• Support PoC, deployment, testing and production rollout.

Requirements

• Bachelor’s degree in Computer Science, Information Technology or a related field.

• At least 3 years of relevant professional experience in solution architecture, cloud architecture, enterprise application development or AI engineering.

• Hands-on experience with LLM-based applications, RAG, AI agents or workflow automation.

• Experience with OpenAI API, Anthropic API, Amazon Bedrock or similar AI platforms.

• Strong understanding of APIs, databases, system integration and cloud architecture.

• Experience with AWS, Azure or Google Cloud.

• Knowledge of authentication, access control, security and enterprise data protection.

• Good communication and problem-solving skills.

• Able to communicate technical solutions clearly to both technical and non-technical stakeholders.

Preferred Skills

• Amazon Bedrock

• AWS Lambda, S3, RDS/PostgreSQL, DynamoDB and OpenSearch

• pgvector or other vector databases

• LangGraph or similar AI orchestration frameworks

• Structured outputs and tool calling

• AI evaluation and guardrails

• Terraform or AWS CDK

• Enterprise system integration