Job Description
AI LEAD — AGENTIC AI CONSULTANT
Charlotte, NC · New York, NY · Hybrid / Remote Options Available
Full-Time · Senior / Principal Level · Technology Consulting
Function
AI & Digital Transformation — Agentic Systems Practice
Level
Principal Consultant
Locations
Charlotte, NC | New York, NY | Hybrid
About the Role
We are looking for a visionary AI Lead to spearhead the design, development, and delivery of enterprise-grade Agentic AI solutions. In this role you will combine deep technical expertise in large language models (LLMs), autonomous agent orchestration, and multi-agent frameworks with a sharp consulting mindset to drive measurable business value for Fortune 500 clients across financial services, healthcare, and retail verticals.
As the practice lead you will own the end-to-end agentic AI delivery lifecycle — from opportunity identification and solution architecture through implementation, change management, and post-deployment optimization — while simultaneously growing a high-performing team and shaping internal AI methodology.
LLM / GenAI
Multi-Agent Systems
RAG Pipelines
AI Strategy
Enterprise Architecture
Key Responsibilities
Client Delivery & Solutioning
• Architect and lead delivery of Agentic AI systems using frameworks such as LangGraph, AutoGen, CrewAI, or custom orchestration layers on Azure / AWS / GCP.
• Design multi-agent pipelines incorporating reasoning, planning, tool-use, memory, and feedback loops tailored to enterprise workflows.
• Partner with C-suite and senior stakeholders to translate ambiguous business challenges into well-scoped AI roadmaps and delivery plans.
• Conduct AI readiness assessments, data audits, and opportunity prioritization workshops for prospective clients.
Technical Leadership
• Define reference architectures for RAG, tool-augmented agents, AI gateways, and human-in-the-loop workflows.
• Drive responsible AI governance — including bias audits, hallucination mitigation, prompt-injection hardening, and model risk frameworks.
• Establish engineering best practices: CI/CD for ML, LLMOps, evaluation harnesses, and performance benchmarking.
• Stay at the cutting edge — evaluate emerging models (GPT-o3, Claude 3.x, Gemini Ultra, open-source LLMs) and tooling for fit-for-purpose applicability.
Practice & Business Development
• Originate and expand client relationships; contribute to proposals, RFP responses, and Statements of Work.
• Build and scale the agentic AI practice — create reusable accelerators, IP assets, and delivery playbooks.
• Mentor and develop a team of AI engineers, ML scientists, and business analysts; conduct performance reviews and career development planning.
• Represent the firm at conferences, publish thought-leadership content, and contribute to the external AI community.
Required Qualifications
• 8+ years of overall technology experience with at least 3 years focused on AI / ML systems in a consulting, product, or enterprise engineering capacity.
• Proven hands-on experience designing and deploying LLM-based applications and autonomous agent systems in production environments.
• Proficiency in Python and relevant AI/ML libraries (LangChain, LlamaIndex, Hugging Face Transformers, PyTorch / TensorFlow).
• Deep familiarity with vector databases (Pinecone, Weaviate, pgvector), semantic search, and knowledge graph integration.
• Experience with cloud AI platforms: Azure OpenAI Service, AWS Bedrock, or Google Vertex AI.
• Strong understanding of prompt engineering, fine-tuning (LoRA, QLoRA, RLHF), and model evaluation methodologies.
• Exceptional executive communication skills — ability to present complex AI concepts to non-technical senior leadership.
• Bachelor’s or Master’s degree in Computer Science, Data Science, AI, Engineering, or a closely related field.
Preferred Qualifications
• Industry certifications: AWS Certified ML Specialty, Google Professional ML Engineer, Azure AI Engineer Associate.
• Experience with agentic frameworks at scale: LangGraph, Microsoft Semantic Kernel, Autogen, or similar.
• Background in financial services, healthcare, or retail — understanding of sector-specific regulatory and compliance constraints.
• Published research, patents, open-source contributions, or conference speaking on AI/ML topics.
• Prior consulting experience at a Big-4, boutique AI firm, or hyperscaler professional services division.
• Familiarity with enterprise integration patterns, API management, and MLOps tooling (MLflow, Weights & Biases, SageMaker Pipelines).
