AI Large Language Model (LLM) Technology Architecture Senior Manager/Associate Director

AI Large Language Model (LLM) Technology Lead/Principal Architect

Career Level: Senior Manager / Associate Director

As a Lead or Principal AI Architect, you will serve as the definitive technical authority on AI architecture within client engagements and across the practice. You will own the complete, end to end architecture of advanced AI platforms and solutions - spanning classical machine learning, generative AI, and agentic systems - ensuring every domain of the architecture is cohesively designed, technically sound, and purposefully aligned to client business objectives and enterprise grade standards.

In this role you will operate at the executive level - working closely with CIOs, CTOs, and senior business leaders to help shape and articulate the enterprise AI strategy. You will connect business goals, priorities, and transformation agendas to a coherent technical vision, ensuring AI investments are purposeful, sequenced, and positioned to deliver lasting competitive advantage. This strategic partnership distinguishes you as both a trusted advisor and a principal architect.

In this role, you will lead and integrate the work of multiple domain architects and subject matter specialists - across areas such as agentic application design, AI security and trust, AI operations and observability, data and knowledge engineering, and model platforms and inference - providing the architectural vision, technical governance, and cross domain coherence that bind their contributions into a unified, enterprise ready system. You will set the architectural direction, resolve cross domain tensions, and make the critical design decisions that shape the entire AI solution. Beyond client delivery, you will be recognized as a thought leader and leading authority in AI - staying at the forefront of the latest research, emerging standards, industry innovations, and evolving technology landscapes. You will actively shape the practice's AI architecture point of view, contribute to internal knowledge frameworks and reusable assets, and represent the firm externally through publications, conference engagements, and client advisory conversations.

You will evaluate and make definitive decisions on design patterns, technical frameworks, reference architectures, and technology selections - balancing innovation with pragmatism to deliver systems that are robust, scalable, and built to last. This includes providing architectural oversight across AI agent ecosystems encompassing multi agent orchestration, tool use, skill use, and memory systems, as well as foundation model integration, fine tuning strategies, and classical ML model deployment within cohesive, production ready platforms. You will be accountable for ensuring the complete architecture meets the most rigorous non functional requirements across security, observability, governance, performance, and scalability - and that these concerns are addressed holistically and consistently across all domains. You will produce and govern the authoritative architecture artifacts that guide delivery at scale - including architecture decision records (ADRs), reference architectures, component and data flow diagrams, and integration specifications - and provide the executive level technical leadership that gives cross functional engineering teams, domain architects, and client stakeholders the clarity and confidence to execute.

• Partner with CIOs, CTOs, and business leaders to shape the enterprise AI strategy, connecting business goals to a coherent, sequenced technical vision
• Lead enterprise AI assessments and build enterprise AI implementation roadmaps that sequence investments for lasting competitive advantage
• Own the complete, end to end technical solution for complex AI platforms - ensuring every domain is cohesively designed and aligned to business objectives and enterprise standards
• Translate the governing architecture principles into a concrete, defensible technical solution that domain teams build against
• Build innovative prototypes and proofs of concept hands on, using emerging technologies to de risk decisions and prove value early
• Perform technology assessments and comparisons, making definitive, evidence based recommendations on tools, frameworks, and platforms
• Set the architectural direction for model and tool agnostic multi agent ecosystems - orchestration, memory, and tool/skill use - governed through a registry bound AI Gateway
• Establish the agent registry and certification model that mandates no uncertified agent reaches production
• Define memory as a first class abstracted platform service, decoupled from any underlying vendor engine
• Define the foundation model and inference strategy - adaptation, fine tuning, and dynamic cost/quality/latency aware routing
• Set the standards for high throughput, low latency inferencing and classical ML deployment within unified, production ready platforms
• Own the architecture of the enterprise context layer - knowledge graphs, ontologies, vector search, and semantic retrieval - grounding the solution in client knowledge
• Set the design direction for context assembly and memory that manages prompts, context windows, and conversational state across the platform
• Be accountable for security, governance, observability, performance, and scalability addressed holistically and consistently across every domain
• Establish the identity and authorization model - per agent identity, IAM/IAP binding, and defense in depth enforcement
• Define the layered guardrail framework applied at every boundary, balancing protection with performance
• Govern the MCP control plane - registry, gateway, and risk scoring - across all internal and third party servers
• Mandate adopt over build for productized evaluation and observability stacks
• Establish FinOps as a first class concern - usage labelling, gateway enforced budgets, and cost per archetype as a planning input
• Make the definitive decisions on design patterns, reference architectures, frameworks, and technology selections, balancing innovation with pragmatism
• Lead and integrate the work of domain architects and specialists, resolving cross domain tensions into a unified, enterprise ready system
• Build the practice's reusable reference architectures, frameworks, and assets, with a deliberate adopt over build stance
• Conduct deep dive architecture workshops and working sessions with client executives and engineering teams
• Produce and govern the authoritative architecture artifacts - blueprints, reference architectures, ADRs, and integration specifications - that guide delivery at scale
• Serve as a recognized thought leader in AI, shaping the practice's point of view and representing the firm externally through publications and conference engagements

• Bachelor's Degree or equivalent

• Extensive experience designing and deploying enterprise grade advanced AI solutions using agentic, generative, and classical AI/ML across at least one cloud vendor
• Proven experience in the LLM and Generative AI space
• Well versed in architecting and operationalizing LLM driven application architecture patterns
• Deep experience in engineering, machine learning, deep learning, and NLP solutions and applications
• Several years of hands on experience as a machine architect in the industry, designing big data, machine learning, and large scale analytical engineering solutions

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