AI Lead Platform Intelligence & Applied AI Long Term Contract Remote

Job Title:

AI LeadWe are partnering with a leading global organization to identify an AI Lead - Platform Intelligence & Applied AI to join its enterprise delivery team. This role is primarily remote, with occasional on-site meetings as needed. Candidates must be comfortable working Eastern Time business hours.

This is a full-time contract engagement (40 hours per week) open to credentialed professionals located within the United States. Candidates should be prepared to complete standard background screening and verification processes in accordance with applicable laws and company policies.

Role OverviewWe are seeking an AI Lead to serve as the technical authority and strategic driver for how artificial intelligence is designed, implemented, and continuously evolved within our enterprise advisory delivery platform.

This role combines deep technical expertise with strategic vision, ensuring the platform leverages modern AI capabilities in a scalable, reliable, and enterprise-ready manner. The AI Lead will maintain a hands-on understanding of emerging AI technologies while translating research and market advancements into practical solutions that enhance the platform's intelligence and capabilities.

You will help define how models are used, how intelligence is orchestrated, how contextual data is assembled, how agents operate, and how AI performance, trust, and quality are measured at scale.

Key ResponsibilitiesAI Strategy & Market IntelligenceContinuously evaluate developments in:

Large language models (LLMs) and foundation modelsAgent frameworks and orchestration architecturesRetrieval, memory, and contextual intelligence techniquesAI evaluation, safety, and governance frameworksTranslate emerging AI capabilities into:

Platform design principlesProofs of concept and experimental initiativesScalable, production-ready capabilitiesProvide strategic guidance to leadership on the adoption and integration of new AI technologies.

Model & Intelligence ManagementDefine and manage the strategy for model usage across the platform, including:

Model selection and benchmarkingVersioning and lifecycle managementPerformance, cost, and latency optimizationRedundancy and fallback strategiesAbstraction layers supporting multi-vendor model integrationEstablish best practices for:

Prompt and instruction designTool and function callingStructured outputs and deterministic system behavior.

Semantic Routing & OrchestrationDesign and evolve the platform's semantic routing layer, including:

Intent detection and task classificationIntelligent routing to appropriate models, agents, or workflowsContext-aware decision-making based on workspace stateDefine orchestration patterns for:

Multi-step and parallel executionLong-running and asynchronous tasksHuman-in-the-loop workflowsEnsure routing logic is transparent, testable, and continuously optimized.

Agent Architecture & ExecutionDefine the organization's AI agent strategy, including:

When agents should be used versus workflows or direct model callsAgent composition, memory design, and tool integrationGuardrails and behavioral constraintsPartner with engineering teams to implement:

Agent frameworks and runtime infrastructureMonitoring, debugging, and performance management capabilitiesEnsure agents are:

Predictable, auditable, and secureAligned with delivery methodologies and service workflowsSafe for enterprise and client-facing environments.

Workspace Context & Retrieval ArchitectureOwn the design of contextual intelligence and retrieval architecture, including:

Document ingestion, chunking, and enrichment pipelinesVector, keyword, and hybrid retrieval approachesContext assembly across client data, internal knowledge, and engagement artifactsDefine standards for:

Source attribution and transparencyData isolation, privacy, and complianceRelevance, freshness, and system performanceContinuously evaluate emerging approaches to memory, retrieval, and grounding.

AI Evaluation, Testing & TrustEstablish and maintain the platform's AI evaluation and testing framework, including:

Scenario-based and task-based evaluation methodologiesRegression testing for prompts, agents, and routing logicComparative benchmarking across models and configurationsDefine performance metrics for:

Accuracy, relevance, and consistencyCost efficiency and latencyUser trust, explainability, and reliabilityCollaborate with engineering and risk teams to ensure:

Full observability into AI system behaviorSafe deployment processes and controlled experimentation.

Qualifications5+ years of experience working with modern AI systems, machine learning platforms, or intelligent application architectures.

Strong understanding of large language models, agent architectures, and retrieval-augmented generation (RAG) frameworks.

Experience designing and implementing AI-driven enterprise platforms or intelligent systems.

Demonstrated ability to translate emerging AI technologies into scalable production solutions.

Excellent collaboration and communication skills, with the ability to work effectively with engineering, product, and business stakeholders.

Bachelor's degree required in Computer Science, Engineering, Artificial Intelligence, or a related technical field.

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Job ID:

5534