AI LLM Technology Architecture Associate Director

Role Overview

Core Responsibilities

• Lead enterprise AI assessments and implementation roadmaps that sequence investments for business value.
• Own complex AI platform architecture and translate governing principles into delivery-ready technical solutions.
• Build prototypes and proofs of concept to evaluate emerging technologies and de-risk architecture decisions.
• Lead domain architects and specialists across agentic applications, AI security, operations, data, knowledge, and model platforms.
• Define multi-agent orchestration, memory, tool use, registry, gateway, and certification architecture.
• Define foundation model integration, fine-tuning, inference routing, classical ML deployment, and context-layer architecture.
• Establish security, identity, authorization, guardrails, governance, observability, performance, scalability, and FinOps standards.
• Produce and govern blueprints, reference architectures, ADRs, data-flow diagrams, and integration specifications.
• Develop reusable practice assets and represent the firm through thought leadership, publications, and conferences.

Required Qualifications

• Proven experience designing and deploying enterprise-grade advanced AI solutions using agentic, generative, and classical AI or machine learning with at least one cloud vendor.
• Proven experience in the LLM and generative AI space.
• Proven experience architecting and operationalizing LLM-driven application architecture patterns.
• Proven experience engineering machine learning, deep learning, and NLP solutions and applications.
• At least six years of industry experience as a machine learning architect or in big data, machine learning, or large-scale analytical engineering solutions.

Architecture Domains

• Multi-agent orchestration, tool use, skills use, and memory systems.
• Knowledge graphs, ontologies, vector search, semantic retrieval, and context assembly.
• AI security, identity, authorization, governance, observability, and operational controls.
• Foundation models, fine-tuning, inference optimization, and model routing.