AI Solutions Architect -GenAI & Enterprise AI Strategy
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AI Summary
The AI Solutions Architect will lead the strategy, design, and implementation of enterprise-grade Generative AI and agentic solutions across finance and corporate functions. This role involves continuous benchmarking of LLMs, optimizing AI performance, and acting as a strategic advisor to executive leadership.
Title: AI Solutions Architect (GenAI & Enterprise AI Strategy)
Location: Warren, New Jersey (remote)
Duration: Long Term
Experience:
15+ Years Overall Experience
7+ Years in AI/ML Solutions
3+ Years in Generative AI and LLM-based Architectures
7+ Years in AI/ML Solutions
3+ Years in Generative AI and LLM-based Architectures
Ideal Candidate Profile
A senior AI leader and hands-on architect who can seamlessly bridge business strategy, enterprise architecture, GenAI innovation, and executive stakeholder management, while continuously evaluating emerging AI technologies and ensuring we remains at the forefront of enterprise AI adoption.
Role Summary
We are seeking a highly experienced AI Solutions Architect to lead the evaluation, architecture, implementation, and optimization of enterprise AI solutions across Finance and Corporate Functions. This is a strategic, customer-facing role requiring deep expertise in Generative AI, Agentic AI, Large Language Models (LLMs), and AI platform architecture. The ideal candidate will possess strong hands-on expertise across leading foundation models including OpenAI GPT, Claude, Gemini, Llama, Mistral, Cohere, and emerging AI platforms. The individual will continuously assess new model releases, benchmark capabilities, identify business applicability, and drive AI adoption while ensuring measurable business value, governance, security, and cost optimization. This role will work directly with senior business leaders, particularly within Finance and Executive Leadership teams, to identify opportunities where AI can improve productivity, decision quality, operational efficiency, and reduce overall AI consumption costs through advanced prompt engineering, model selection, and architecture optimization.
Key Responsibilities
AI Strategy & Architecture
- Define enterprise AI architecture and roadmap aligned with business objectives.
- Design scalable, secure, and production-ready GenAI solutions leveraging multiple LLM providers.
- Establish AI reference architectures, design patterns, governance standards, and best practices.
- Lead architecture reviews and provide technical leadership across AI initiatives.
- Build frameworks for AI adoption across Finance, Operations, Risk, Underwriting, Claims, and Corporate functions.
LLM Evaluation & Benchmarking
- Continuously evaluate emerging LLMs and new releases from:
- OpenAI (GPT Family)
-
- Anthropic (Claude)
- Perform comparative assessments across:
- Accuracy
-
- Response quality
-
- Reasoning capability
-
- Hallucination rates
-
- Performance
-
- Security posture
-
- Cost per token
-
- Enterprise readiness
- Build model evaluation scorecards and recommendation frameworks.
Prompt Engineering & Token Optimization
- Design advanced prompting frameworks for enterprise use cases.
- Partner with Finance executives and business users to optimize prompts for:
- Cost reduction
-
- Token consumption efficiency
-
- Output accuracy
-
- Response consistency
- Develop reusable prompt libraries and knowledge repositories.
- Establish AI performance measurement frameworks and ROI tracking.
- Drive initiatives to reduce LLM operational costs while maintaining output quality.
Agentic AI & AI Automation
- Architect AI agents and multi-agent frameworks.
- Design autonomous workflows leveraging:
- MCP (Model Context Protocol)
-
- Agent-to-Agent communication
-
- Tool Calling
-
- Function Calling
-
- Workflow Orchestration
- Build intelligent AI copilots and virtual assistants for business functions.
Enterprise AI Implementation
- Lead end-to-end delivery of GenAI solutions from ideation through production deployment.
- Design and implement:
- Retrieval Augmented Generation (RAG)
-
- GraphRAG
-
- Knowledge Management Platforms
-
- Enterprise Search Solutions
-
- AI Copilots
-
- Document Intelligence Solutions
- Integrate AI solutions with enterprise applications and data platforms.
Responsible AI & Governance
- Define and implement:
- Responsible AI frameworks
-
- Security and Privacy controls
-
- Model governance
-
- Compliance standards
-
- Guardrails and human-in-the-loop processes
- Ensure enterprise AI solutions meet regulatory and risk requirements.
Executive Advisory
- Act as trusted AI advisor to senior executives.
- Translate complex AI concepts into business outcomes.
- Conduct AI workshops and executive briefings.
- Identify high-value AI use cases and build business cases.
- Support AI investment decisions and vendor evaluations.
Required Technical Skills
Large Language Models
- GPT-4o / GPT-5
- Claude Sonnet / Opus
- Gemini
- Llama
- Mistral
- Cohere
- Open-source LLM ecosystems
AI Architecture
- GenAI Architecture
- Agentic AI
- Multi-Agent Systems
- AI Copilots
- Enterprise AI Platforms
- LLMOps
- AI Governance
RAG & Knowledge Systems
- RAG
- GraphRAG
- Hybrid Search
- Vector Databases
- Embeddings
- Semantic Search
AI Frameworks
- LangChain
- LangGraph
- Semantic Kernel
- AutoGen
- CrewAI
- LlamaIndex
Cloud & AI Platforms
- Azure AI Foundry
- Azure OpenAI
- AWS Bedrock
- Google Vertex AI
- Databricks AI
Programming
- Python
- REST APIs
- Microservices
- Enterprise Integration Patterns
Leadership Expectations
- Executive presence and communication skills.
- Ability to influence C-level stakeholders.
- Strong consulting and problem-solving capabilities.
- Experience leading cross-functional AI transformation programs.
- Ability to mentor architects, engineers, and business teams.
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