Technical Architect - AI Engineering

Capital Numbers is seeking a hands-on Technical Architect – AI Engineering to design, build, and deploy production-grade AI systems while leading a high perform

Roles &

RESPONSIBILITIES

AI System Architecture: Design: Architect, build, and optimize scalable RAG (Retrieval-Augmented Generation) pipelines, LLM-driven applications, and multi-agent AI systems. Enterprise Integration: Integrate LLMs, custom embeddings, vector databases, and APIs with complex enterprise data layers and existing business applications. System Optimization: Reliability: Solve critical AI deployment challenges, including retrieval quality improvement, hallucination mitigation, latency reduction, and query cost management. Technical Leadership: Mentorship: Drive code reviews, establish coding standards, unblock developers, and mentor a small team of software and AI engineers. Security & Infrastructure: Implement AI security best practices, ensure enterprise data privacy, and oversee deployment across cloud environments (AWS / Azure / GCP). Skills: Core Languages: Frameworks: Python, Lang Chain, Llama Index, Open AI APIs, Hugging Face AI Architecture: Generative AI, RAG Pipelines, AI Agents, Vector Databases (Pinecone, Chroma DB, Weaviate, Qdrant), Prompt Engineering, Embeddings Cloud: Dev Ops: AWS / Azure / GCP, Docker, API Integration, Microservices, CI/CD Leadership: Technical System Design, Code Reviews, Agile Development, Team Mentoring Domain Advantage (Preferred): Healthcare tech experience (EMR, HMIS, healthcare data privacy standards) Qualification & Experience Educational Background: Bachelor’s or Master’s degree in Computer Science, Information Technology, Software Engineering, or a related technical field. Production Track Record: Proven background in moving LLM/Gen AI prototypes into production enterprise environments. Domain Expertise: Experience working on complex data architectures; exposure to Healthcare/EMR/HMIS platforms is strongly preferred.