AI Architect

Job Title: AI Architect

Location: Charlotte, NC / Dallas, TX / Iselin, NJ (Hybrid)
Employment Type: Contract 

Job Duties: Role Overview:

We are seeking a AI Architect to serve as a hands-on practitioner and core technical visionary. This is a rare, high-impact role requiring deep expertise in Generative AI, distributed systems, and agentic architectures. You will act as the central design authority for our GenAI capabilities within a matrixed organization, bridging internal platform development, third-party vendor reviews, and cutting-edge agentic workflows.

Your primary mandate is to "push the thinking"—elevating our AI strategy while remaining deeply hands-on. You will oversee all GenAI use cases, driving architectural excellence across cloud, on-premise, and edge environments, with a specific focus on applications within the regional banking and financial services sector.

Key Responsibilities:

GenAI Architecture & Thought Leadership:

• Serve as the ultimate technical authority for GenAI architecture across the enterprise, reviewing and guiding all AI/ML use cases within a matrixed organization.
• Push the boundaries of our technical vision, acting as a forward-thinking catalyst for how GenAI is built and deployed.
• Lead the architectural review process for all third-party AI integrations coming into the bank (e.g., ServiceNow, Five9, Pega), ensuring they meet strict security, performance, and integration standards.

Agentic Stack & AI Platform Engineering:

• Spearhead the growth and development of our agentic stack, designing agentic frameworks that incorporate robust workflow (WF) logic.
• Architect sophisticated retrieval systems and agent data stacks, utilizing vector databases, hybrid search, BM25, and graph-based reasoning.
• Implement solutions for externalized long-term memory, contextual data freshness, and Model Context Protocol (MCP) servers.
• Lead prompt and context engineering strategies to maximize model accuracy and reliability.

Infrastructure, Inference & Edge Computing:

• Design, implement, and scale high-performance distributed systems and AI/ML platforms.
• Optimize LLM inference, implementing advanced batching, caching strategies, and load balancing techniques.
• Evaluate and implement dynamic deployment strategies, weighing the trade-offs of deploying small/local LLMs at the edge versus leveraging hyperscaler inferencing via cloud APIs.
• Architect and test distributed API gateways across hybrid (cloud and on-premise) environments.
• Oversee on-premise hardware strategy, including rigorous GPU management, utilization, and thermal/compute optimization.

Minimum Skills Required: Required Qualifications

• Engineering Foundation: 12-15 years experience with strong proficiency in at least one core programming language (e.g., Python, Go, C++) and deep experience building large-scale distributed systems.
• GenAI & LLM Expertise: 5-7 years hands-on, practitioner-level experience with LLM inference optimization, fine-tuning, and deployment strategies.
• Agentic Architectures: 3-5 years experience with a proven track record of building complex agentic systems, evaluation frameworks, and advanced retrieval pipelines (RAG, Vector DBs, Graph reasoning).
• Cloud & Infrastructure: 10-12 years extensive experience with Kubernetes, Cloud Infrastructure (AWS, Google Cloud Platform, or Azure), and managing high-availability platforms.
• Hardware / On-Premise Knowledge: 8-10 years experience and understanding of GPU orchestration, resource management, and hardware optimization in on-premise or hybrid data centers.
• Strategic Communication: 12-15 years experience and ability to navigate a matrixed organization, translate complex technical trade-offs to leadership, and rigorously evaluate third-party enterprise platforms.

Nice to Have:

• Domain experience in the Banking or Financial Services industry
• Interest or hands-on experience in Blockchain technologies and decentralized frameworks

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