What are the responsibilities and job description for the AI Solutions Architect (Generative & Agentic AI) position at Madison-Davis, LLC?
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