AI LLM Technology Architecture Senior Manager

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. A defining dimension of this role is your ability to 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 with client leadership 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 binds 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. Your perspective on where AI is heading will be sought by clients, practice leadership, and peers alike. 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, skills 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. Your work will be instrumental in defining how the most ambitious clients adopt, scale, and lead with AI - setting a standard for what advanced AI architecture can and should look like, and delivering transformational, lasting business value.

THE WORK

• 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

• Minimum of 8 years of experience in designing & deploying enterprise grade advanced ai solutions using agentic, generative and classical AI/ML using at least one cloud vendor.
• Minimum of 2 years of experience in the LLM and Generative AI space.
• Minimum of 2 years of experience architecting and operationalizing LLM driven application architecture patterns.
• Minimum of 6 years in engineering, machine learning, deep learning and NLP solutions and applications. Minimum of 6 years of experience as a machine architect in the industry designing big data, machine learning. large scale analytical engineering solutions

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