AI Tech Lead – Platform Intelligence & Applied AI

Role Overview

We are hiring an AI Lead to serve as the technical authority and strategic driver for how artificial intelligence is designed, implemented, and evolved within Advisory’s enterprise delivery platform. This role is responsible for maintaining a deep, hands-on understanding of modern AI systems, monitoring market and research trends, and translating those advancements into practical, enterprise-ready platform capabilities. The AI Lead defines how models are used, intelligence is orchestrated, context is assembled, agents behave, and AI quality and trust are measured at scale.

Core Responsibilities

AI Strategy & Market Intelligence

• Continuously track and evaluate:
  • LLM and foundation model advancements
  • Agent frameworks and orchestration patterns
  • Retrieval, memory, and context management techniques
  • AI evaluation, safety, and governance approaches
• Translate emerging AI trends into:
  • Platform design principles
  • Proofs of concept and experiments
  • Scalable, production-ready capabilities
• Advise leadership on when and how new AI capabilities should be adopted.

Model & Intelligence Management

• Own the strategy for LLM and model usage across the platform, including:
  • Model selection and benchmarking
  • Versioning and lifecycle management
  • Cost, performance, and latency trade-offs
  • Fallback and redundancy strategies
  • Abstraction layers that enable multi-vendor model support
• Establish best practices for:
  • Prompt and instruction design
  • Tool and function calling
  • Structured outputs and determinism

Semantic Routing & Orchestration:

• Design and evolve the platform’s semantic routing layer, including:
  • Intent detection and task classification
  • Routing to appropriate models, agents, or workflows
  • Context-aware decisioning based on workspace state
• Define orchestration patterns for:
  • Multi-step and parallel execution
  • Long-running and asynchronous tasks
  • Human-in-the-loop controls
• Ensure routing logic is transparent, testable, and tunable.

Agent Architecture & Execution

• Define the firm’s agent strategy, including:
  • When to use agents vs. workflows vs. direct LLM calls
  • Agent composition, memory, and tool access
  • Guardrails and behavioral constraints
• Partner with engineering to implement:
  • Agent frameworks and runtime infrastructure
  • Monitoring and debugging capabilities
• Ensure agents are:
  • Predictable and auditable
  • Aligned to service methods and delivery workflows
  • Safe for enterprise and client-facing use

Workspace Context & RAG Architecture

• Own the design of contextual intelligence within workspaces, including:
  • Document ingestion, chunking, and enrichment strategies
  • Vector, keyword, and hybrid retrieval approaches
  • Context assembly across client data, firm IP, and engagement artifacts
• Define standards for:
  • Source attribution and transparency
  • Data isolation and compliance
  • Relevance, freshness, and performance
  • Continuously evaluate new approaches to memory, retrieval, and grounding.

AI Evaluation, Testing & Trust

• Establish the platform’s AI evaluation and testing framework, including:
  • Task-based and scenario-driven evaluations
  • Regression testing for prompts, agents, and routing logic
  • Comparative benchmarking across models and configurations
• Define metrics for:
  • Accuracy, relevance, and consistency
  • Cost efficiency and latency
  • User trust and explainability
• Partner with engineering and risk teams to ensure:
  • Observability into AI behavior
  • Safe deployment and controlled experimentation

Qualifications:

• 5+ years of proven expertise
• Bachelor's degree required.