AI Business Strategy Manager

AI Business Strategy Manager at Incedo Inc. AI Business Strategy Manager at Incedo Inc. in Fort Mill, South Carolina Posted in 1 day ago.

Type:

full-time

Job Description:

AI LEAD - AGENTIC AI CONSULTANT

Charlotte, NC

• New York, NY
• Hybrid 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).