AI Lead - Platform Intelligence & Applied AI

AI Lead – Platform Intelligence & Applied AI

Position: AI Lead – Platform Intelligence & Applied AI

Location: Chicago, IL (Remote)

Duration: 6-12 Months

Key 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
• Establish best practices for: prompt and instruction design, skill and tool 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

• Consult on 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, continuous improvement loops based on real usage

Platform Enablement & Collaboration

• Work closely with: platform engineering teams, product and design partners, consulting and delivery leaders
• Provide technical guidance on: how AI capabilities should be embedded into platform features, where AI adds leverage vs. complexity
• Support enablement through: technical documentation and reference architectures, internal education and design reviews, advisory support for high-impact use cases

Governance & Responsible AI

• Define technical guardrails that support: security, privacy, and data residency, responsible AI principles, regulatory and client requirements
• Ensure AI systems are: explainable where required, observable and auditable, designed for controlled evolution over time

Required Skills

• Strong experience with AI strategy, platform intelligence, and applied AI solutions.
• Hands-on experience with LLMs, foundation models, and model lifecycle management.
• Experience designing and implementing agent architectures and orchestration frameworks.
• Strong understanding of RAG architecture, retrieval strategies, embeddings, vector databases, and context management.
• Experience with prompt engineering, structured outputs, tool calling, and AI workflow design.
• Experience designing semantic routing, intent classification, and AI decisioning systems.
• Strong knowledge of AI evaluation frameworks, benchmarking methodologies, testing strategies, and observability.
• Experience implementing AI governance, security, privacy, and responsible AI practices.
• Ability to translate emerging AI technologies into enterprise-scale platform capabilities.
• Strong collaboration skills across engineering, product, design, consulting, and leadership teams.