AI Solutions Architect (Generative AI & Enterprise Solutions)

Role overview

AI Solutions Architect responsible for designing and delivering enterprise-scale AI solutions—from concept through production—translating business challenges into scalable, secure, cloud-native architectures.

Responsibilities

• End-to-end solution architecture for AI initiatives: discovery, solution design, deployment, and production monitoring.
• Translate business requirements into AI-first solutions and recommend build vs. buy vs. augment approaches.
• Design secure, cloud-native architectures across applications, APIs, data platforms, AI services, and infrastructure.
• Define reusable architecture patterns for:
  • RAG
  • Agentic AI / multi-agent systems
  • Model routing
  • MCP/tool integrations
  • AI orchestration
• Technical leadership for client work:
  • Lead solutioning for proposals, RFPs, POCs, and discovery engagements.
  • Produce architecture diagrams, technical documentation, implementation roadmaps, and effort estimates.
  • Present solutions to clients and executive stakeholders.
  • Partner with delivery/engineering teams to ensure successful implementation.
• AI governance & engineering excellence:
  • Establish standards for security, privacy, compliance, AI governance, model risk, and cost optimization.
  • Create Architecture Decision Records (ADRs), technical standards, and non-functional requirements.
  • Evaluate and standardize AI development tools (e.g., GitHub Copilot, Claude Code, Cursor, Amazon Q, Gemini CLI).
  • Mentor engineers and architects; drive adoption of AI-assisted development best practices.

Requirements

• Bachelor’s or Master’s degree in Computer Science, Software Engineering, or related field.
• 8+ years software engineering experience, including experience as a Solution Architect/Technical Architect/Tech Lead/Lead Engineer on enterprise applications.
• Proven end-to-end enterprise experience across frontend, backend, APIs, data, and cloud infrastructure.
• Hands-on expertise with:
  • Large Language Models (LLMs)
  • Retrieval-Augmented Generation (RAG)
  • Agentic AI / multi-agent systems
  • Prompt & context engineering
  • Model evaluation & selection
  • Model Context Protocol (MCP) or similar integrations
• Experience designing cloud-native solutions on Azure, AWS, or GCP.
• Experience with AI frameworks such as LangChain, LangGraph, LlamaIndex, Semantic Kernel, CrewAI, AutoGen (or similar).
• Strong understanding of microservices, Kubernetes, containers, APIs, distributed systems, and vector databases.
• Experience leading solutioning for RFPs, proposals, POCs, feasibility studies, and effort estimation.
• Hands-on experience with AI developer tools such as GitHub Copilot, Claude Code, Cursor, Amazon Q, Gemini CLI (or similar).
• Excellent communication skills across technical teams and executive stakeholders.
• Experience establishing architecture governance, security, compliance, and AI best practices.
• Experience mentoring engineers and driving adoption of modern engineering practices.

Preferred qualifications

• Experience with legacy modernization and cloud migration.
• Knowledge of MLOps/LLMOps, DevSecOps, CI/CD, Terraform, and Infrastructure as Code.
• Cloud and AI certifications (Azure/AWS/GCP) are highly desirable.