Own the end-to-end AI architecture across LLM orchestration, retrieval, enterprise data integration, and deployment.
Define architectural standards, best practices, and reusable AI patterns across the platform.
Evaluate architectural trade-offs involving latency, cost, model quality, scalability, security, and maintainability.
Define the long-term AI strategy in collaboration with the Delivery Lead and engineering leadership.
Design intelligent agent architectures, including tool calling, retrieval, planning, multi-step reasoning, and state management.
Design AI integrations with enterprise data platforms, semantic models, APIs, and knowledge repositories.
Define AI evaluation strategies covering quality, hallucination detection, grounding, latency, and cost optimization.
Design observability and monitoring capabilities for AI applications, including prompt performance, model behavior, and operational telemetry.
Collaborate with cloud infrastructure and security teams to ensure secure, resilient, and compliant deployments.
Contribute directly to proof-of-concepts and critical implementation efforts when needed.

10+ years of professional experience in software engineering, including designing and architecting enterprise software solutions.
3+ years of experience designing and delivering production AI solutions leveraging Large Language Models.
Strong experience designing enterprise AI systems using Retrieval-Augmented Generation (RAG), agentic AI, tool calling, prompt orchestration, and multi-agent workflows.
Hands-on experience with Python and modern backend frameworks such as FastAPI or equivalent.
Experience with Azure AI services, including Azure AI Foundry, Azure AI Search, Azure OpenAI, or equivalent AI platforms.
Experience building AI applications using frameworks such as Semantic Kernel, AutoGen, Microsoft Agent Framework, LangGraph, LangChain, or similar orchestration frameworks.
Strong understanding of enterprise data integration, APIs, vector databases, semantic search, and knowledge management architectures.
Experience designing AI evaluation, monitoring, observability, and governance strategies.
Strong understanding of Azure architecture and cloud-native application design.
Experience implementing CI/CD pipelines and Infrastructure-as-Code.
Familiarity with cloud security, identity management, RBAC, and enterprise governance principles.

AI Solutions Architect

Key skills