AI Architect

Project overview

Role: AI Architect

Project details

Charlotte NC (3 days Work from Office).

Contract role

Need senior resources who needs to do hands and Collaborate with Product Owners, Data Architects, Engineering Teams, Data Scientists, and Business Stakeholders.

Mentor senior engineers and AI developers on architecture best practices

About the Role

We are seeking a highly experienced AI Architect to lead the design, strategy, and implementation of enterprise-scale AI solutions powered by Agentic AI, LLMs/SLMs, Autonomous Agents, and Generative AI platforms. The role will focus on architecting multi-agent systems, supervisory AI workflows with human-in-the-loop controls, evaluating agentic skills, design integration, workflows, and driving AI-led transformation across data and modernization initiatives.

This role requires strong collaboration with business and technology stakeholders to translate AI innovation into scalable, governed, and business-aligned solutions.

Key Responsibilities

Required Skills & Expertise

Agentic AI & Autonomous Systems

Generative AI & Foundation Models

AI Engineering & Platform Architecture

Cloud & Enterprise Platforms

AI Tools & Developer Productivity

Hands-on proficiency with modern AI-assisted engineering tools including:

Knowledge & Retrieval Systems

Preferred Qualifications

RAG & Knowledge Intelligence

Platform Engineering

Stakeholder & Team Leadership

Required Technical Skills

Core Engineering

Agentic AI

Generative AI

RAG & Search

Vector Databases

Cloud Platforms

Data & Databases

DevOps & Platform Engineering

Job Facts: Contract W2 | 12 Months | Hybrid | $70 - $80/hr

Skills: API, Amazon Web Services, Analytics, Data Quality, Decision-making, Design Patterns, DevOps, Continuous Delivery, Continuous Integration, Data Engineering, Data Governance, Database, Artificial Intelligence, Cloud Architecture, Cloud Computing, Collaboration, Communication, Google Cloud Platform, Innovation, Language Models, Mapping, Mentorship, Evaluation, Meta-data Management, Microservices, Microsoft, Microsoft Azure, FOCUS, Flask, Generative Artificial Intelligence (AI), Good Clinical Practice, Scalability, Python, Reasoning, Regulatory Compliance, Research, Risk Management, Roadmaps, Messaging, Microsoft Certified Professional, Optimization, Orchestration, Presentations, Semantic Search, Semantics, Software Design, Supervision, Use Cases, Workflow

Reference: Dice Id: 10114281 | Position Id: 9137082

• Define architecture and design principles, support planning of technical implementation roadmaps.
• Partner with client stakeholders, Product Owner to identify, prioritize, and shape AI use cases aligned to business and data transformation goals.
• Architect autonomous agent platforms with supervisory controls, human-in-the-loop workflows, governance, and auditability.
• Design multi-agent systems leveraging planning, reasoning, memory, orchestration, and tool-calling capabilities.
• Define agent communication and interoperability frameworks using MCP (Model Context Protocol) and modern AI integration patterns.
• Evaluate and recommend optimal hybrid strategy for LLM/SLM and deterministic approach for enterprise AI applications, automation platforms, and transpiler solutions.
• Research emerging AI technologies, frameworks, and industry trends to provide strategic recommendations.
• Develop AI governance, safety, observability, security, and responsible AI frameworks.
• Define agent mapping, policy recommendations, and operating models for enterprise AI adoption.
• Collaborate with engineering, product, and business teams to establish reusable AI architecture patterns and standards.
• Support executive stakeholders through architecture reviews, technology assessments, workshops, and strategic advisory engagements.
• Multi-Agent Architectures
• Agentic AI Workflows
• Autonomous Agent Platforms
• Human-in-the-Loop AI Systems
• Supervisory AI Architectures
• Agent Planning, Reasoning, and Memory
• Tool Calling & Function Calling
• Agent Governance and Guardrails
• MCP (Model Context Protocol)
• OpenAI Models
• Anthropic Claude
• Azure OpenAI
• Gemini
• Llama Models
• Large Language Models (LLMs)
• Small Language Models (SLMs)
• Prompt Engineering
• Model Evaluation & Benchmarking
• AI Safety and Responsible AI
• Python
• FastAPI and API-Driven Architectures
• Distributed Systems
• Cloud-Native AI Platforms
• RAG Architectures
• Vector Databases and Semantic Search
• AI Observability and Monitoring
• Microsoft Azure
• AWS
• Google Cloud Platform
• Docker
• Kubernetes
• Terraform
• CI/CD and DevOps Practices
• Microsoft Copilot
• GitHub Copilot
• Devin
• Claude Code
• AI Coding Agents
• AI-Powered Development Workflows
• Retrieval Augmented Generation (RAG)
• Semantic Search
• Vector Search
• Hybrid Retrieval
• Knowledge Graphs
• Embeddings
• Re-ranking Models
• Context Engineering
• Experience architecting enterprise AI platforms in regulated environments.
• Experience designing supervisory AI systems with governance and compliance controls.
• Strong understanding of AI risk management, explainability, and policy frameworks.
• Experience with AI-driven modernization, code conversion, transpilers, and automation platforms.
• Knowledge of Data Engineering, Data Governance, Data Quality, Metadata Management, and Analytics ecosystems.
• Cloud Architecture certifications (Azure, AWS, or Google Cloud Platform) preferred.
• Architect enterprise-scale Retrieval-Augmented Generation (RAG) solutions.
• Design hybrid retrieval systems using embeddings, vector search, semantic search, knowledge graphs, and reranking models.
• Build scalable pipelines for document intelligence, metadata discovery, semantic mapping, and contextual reasoning.
• Optimize retrieval quality, latency, grounding, and hallucination mitigation.
• Architect cloud-native microservices using Python and FastAPI.
• Design distributed event-driven architectures leveraging APIs, messaging systems, and asynchronous processing.
• Lead implementation of CI/CD, Infrastructure as Code, observability, monitoring, and platform security.
• Ensure production readiness, resiliency, scalability, and cost optimization.
• Collaborate with Product Owners, Data Architects, Engineering Teams, Data Scientists, and Business Stakeholders.
• Mentor senior engineers and AI developers on architecture best practices.
• Drive technical decision-making, architecture reviews, and innovation initiatives.
• Support customer engagements, solution workshops, and executive presentations.
• Expert-level Python development
• FastAPI, Flask, Django
• REST APIs and Microservices Architecture
• Async Programming (asyncio)
• Design Patterns and Clean Architecture
• Distributed Systems Architecture
• LangGraph
• LangChain
• CrewAI
• AutoGen
• LlamaIndex
• Multi-Agent Orchestration
• Tool Calling and Function Calling
• Agent Memory and Planning Frameworks
• OpenAI
• Claude
• Fine-tuning and Model Evaluation
• AI Safety and Guardrails
• Retrieval-Augmented Generation (RAG)
• Vector Search Architecture
• Hybrid Search
• Knowledge Graph Integration
• Pinecone
• ChromaDB
• Qdrant
• Milvus
• Weaviate
• pgVector
• PostgreSQL
• MongoDB
• SQL Server
• Snowflake
• Databricks
• Data Lake Architectures
• OpenShift
• GitHub Actions
• Azure DevOps
• Jenkins
• OpenTelemetry
• Start: flexible
• Duration: 12 Months
• Remote: hybrid
• Location: Charlotte, NC, US, US