Madison-Davis
AI Solutions Architect (Generative & Agentic AI)
Madison-Davis, Atlanta, Georgia, United States, 30383
Design the backbone of enterprise-scale AI systems.
We’re hiring a hands‑on AI Solutions Architect to lead the technical vision for next‑gen GenAI and agentic AI platforms at one of the largest financial institutions in the world. This is a high‑impact architecture role focused on LLM integration, retrieval‑augmented generation (RAG), and enterprise orchestration — without management responsibilities.
What You’ll Own :
Architect end‑to‑end systems for GenAI workloads, including APIs, vector stores, and orchestration layers
Define scalable, secure patterns for LLM usage across multiple business lines
Drive integration of OpenAI, Azure AI, LangChain, and embeddings infrastructure
Partner with platform, cloud, and data teams to enable performant deployment pipelines
Develop reference architectures and technical guidance for teams building with generative AI
Work cross‑functionally with security, governance, and compliance stakeholders to align on standards
You Bring :
10+ years of experience in backend, platform, or ML engineering with a strong architectural focus
Deep technical understanding of GenAI systems, LLM APIs, embedding models, and RAG pipelines
Experience deploying containerized AI services on cloud (Kubernetes, Terraform, Azure or AWS)
Fluency in Python and AI service frameworks (LangChain, LlamaIndex, OpenAI SDKs)
Ability to design systems that are both technically elegant and enterprise‑grade
Strong communication skills — able to create clarity across engineering, governance, and executive audiences
Why This Role?
Total ownership of greenfield GenAI architecture across a major enterprise
Collaborate with elite engineering and AI platform teams
Long‑term project runway backed by strategic investment
Hybrid flexibility, cutting‑edge toolchain, and technical autonomy
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What You’ll Own :
Architect end‑to‑end systems for GenAI workloads, including APIs, vector stores, and orchestration layers
Define scalable, secure patterns for LLM usage across multiple business lines
Drive integration of OpenAI, Azure AI, LangChain, and embeddings infrastructure
Partner with platform, cloud, and data teams to enable performant deployment pipelines
Develop reference architectures and technical guidance for teams building with generative AI
Work cross‑functionally with security, governance, and compliance stakeholders to align on standards
You Bring :
10+ years of experience in backend, platform, or ML engineering with a strong architectural focus
Deep technical understanding of GenAI systems, LLM APIs, embedding models, and RAG pipelines
Experience deploying containerized AI services on cloud (Kubernetes, Terraform, Azure or AWS)
Fluency in Python and AI service frameworks (LangChain, LlamaIndex, OpenAI SDKs)
Ability to design systems that are both technically elegant and enterprise‑grade
Strong communication skills — able to create clarity across engineering, governance, and executive audiences
Why This Role?
Total ownership of greenfield GenAI architecture across a major enterprise
Collaborate with elite engineering and AI platform teams
Long‑term project runway backed by strategic investment
Hybrid flexibility, cutting‑edge toolchain, and technical autonomy
#J-18808-Ljbffr