AI Solutions Architect

AI Solutions ArchitectLocation: Dallas (Hybrid) Total Compensation: Up to $200KWe are a digital engineering firm delivering AI, data, and platform solutions for enterprise clients in regulated industries. Our teams ship production systems that improve customer experience, automate operations, and unlock measurable business outcomes. We value pragmatic architecture, hands-on validation, and strong governance.Position SummaryWe're hiring a Sr. Solution Architect focused on Generative AI to design and deliver enterprise GenAI applications and AI agents. You will combine architecture leadership with hands-on PoC work to validate solutions quickly and scale them responsibly. Success looks like production launches that meet privacy, compliance, and performance standards.What You Will DoArchitect and deliver GenAI applications and AI agents for high-impact use cases in financial services.Design robust RAG pipelines and agentic systems including prompt orchestration, memory, vector search, and context management.Build PoCs/MVPs hands-on to validate approaches and de-risk scale-out.Define evaluation methods, golden datasets, and quality metrics for models, prompts, and retrieval.Partner with product, data, security, and compliance to meet governance, privacy, and audit requirements.Select platforms and services across Azure OpenAI, Vertex AI, Anthropic, OpenAI API, and vector databases such as Pinecone, Weaviate, Chroma, and FAISS.Produce architecture docs, threat models, data flow diagrams, and runbooks.Guide engineering during implementation and support pre-sales as needed.What You Bring10+ years solution architecture with 2+ years leading GenAI solution design.Shipped production GenAI systems and AI agents at enterprise scale.Strong RAG and agentic orchestration expertise.Proficiency building PoCs in Python or TypeScript or C#.Hands-on with vector DBs and embedding workflows.Experience in regulated environments with data privacy, governance, and compliance.Excellent communication and leadership.Nice To HaveRetrieval optimization and reranking experience.Safety and guardrail tooling implementation.Model benchmarking and cost/performance optimization.MLOps/LangOps practices for GenAI.