AI Lead Platform Intelligence & Applied AI

100% remote role

Title: AI Lead - Platform Intelligence & Applied AI

Location: Chicago, IL

Duration: 6-12 Months

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:

o LLM and foundation model advancements

o Agent frameworks and orchestration patterns

o Retrieval, memory, and context management techniques

o AI evaluation, safety, and governance approaches

Translate emerging AI trends into:

o Platform design principles

o Proofs of concept and experiments

o 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:

o Model selection and benchmarking

o Versioning and lifecycle management

o Cost, performance, and latency trade-offs

o Fallback and redundancy strategies

Establish best practices for:

o Prompt and instruction design

o Skill and Tool calling

o Structured outputs and determinism

Semantic Routing & Orchestration

Design and evolve the platform s semantic routing layer, including:

o Intent detection and task classification

o Routing to appropriate models, agents, or workflows

o Context-aware decisioning based on workspace state

Define orchestration patterns for:

o Multi-step and parallel execution

o Long-running and asynchronous tasks

o Human-in-the-loop controls

Ensure routing logic is transparent, testable, and tunable.

Agent Architecture & Execution

Consult on the firm s agent strategy, including:

o When to use agents vs. workflows vs. direct LLM calls

o Agent composition, memory, and tool access

o Guardrails and behavioral constraints

Partner with engineering to implement:

o Agent frameworks and runtime infrastructure

o Monitoring and debugging capabilities

Ensure agents are:

o Predictable and auditable

o Aligned to service methods and delivery workflows

o Safe for enterprise and client-facing use

Workspace Context & RAG Architecture

Own the design of contextual intelligence within workspaces, including:

o Document ingestion, chunking, and enrichment strategies

o Vector, keyword, and hybrid retrieval approaches

o Context assembly across client data, firm IP, and engagement artifacts

Define standards for:

o Source attribution and transparency

o Data isolation and compliance

o 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:

o Task-based and scenario-driven evaluations

o Regression testing for prompts, agents, and routing logic

o Comparative benchmarking across models and configurations

Define metrics for:

o Accuracy, relevance, and consistency

o Cost efficiency and latency

o User trust and explainability

Partner with engineering and risk teams to ensure:

o Observability into AI behavior

o Safe deployment and controlled experimentation

o Continuous improvement loops based on real usage

Platform Enablement & Collaboration

Work closely with:

o Platform engineering teams

o Product and design partners

o Consulting and delivery leaders

Provide technical guidance on:

o How AI capabilities should be embedded into platform features

o Where AI adds leverage vs. complexity

Support enablement through:

o Technical documentation and reference architectures

o Internal education and design reviews

o Advisory support for high-impact use cases

Governance & Responsible AI

Define technical guardrails that support:

o Security, privacy, and data residency

o Responsible AI principles

o Regulatory and client requirements

Ensure AI systems are:

o Explainable where required

o Observable and auditable

o Designed for controlled evolution over time

What Success Looks Like

The platform consistently adopts relevant AI innovations without destabilizing

delivery.

AI behavior is predictable, testable, and trusted by consultants and leadership.

New models, agents, and techniques can be introduced rapidly through well

defined abstractions.

AI capabilities directly improve delivery quality, speed, and consistency across

engagements.