AI Architect

AI Architect

Location:

Miami, FL Duration: 6 months Role: AI Architect Experience

Required:

10–15 years Domain:
Artificial Intelligence / Machine Learning / Enterprise Architecture

• 10–15 years of experience in AI, Machine Learning, or Enterprise Architecture roles.
• Proven experience architecting:
  Retrieval-Augmented Generation (RAG) systems, Vector search solutions, LLM-based knowledge platforms.
• Strong hands-on experience with Python.
• Strong experience with cloud-based ML platforms, particularly Microsoft Azure.
• Hands-on experience with Vector Databases, including Pinecone and DB Vector.
• Experience with Lang Chain, Llama Index, or similar LLM orchestration frameworks.
• Strong experience with Fine-tuning, Embeddings, Vectorization.
• Experience or familiarity with Document chunking, Knowledge graphs, Vectorization.
• Expertise in Document AI, Natural Language Processing (NLP), Unstructured data processing.

• Design and architect enterprise-grade RAG architectures.
• Design scalable vector search and retrieval solutions.
• Architect LLM-powered knowledge platforms.
• Develop AI/ML solutions using Python and cloud ML platforms.
• Design embedding and vectorization strategies.
• Define document processing and chunking approaches.
• Architect knowledge graph and semantic retrieval solutions.
• Apply fine-tuning techniques to improve LLM performance and domain relevance.

• Strong architectural experience with Microsoft Azure.
• Design and implement scalable cloud ML architectures.
• Experience with cloud-based AI/ML services and infrastructure.
• Strong understanding of containerization using Docker and Kubernetes (K8s).
• Experience designing secure, scalable, and highly available AI platforms.

• Strong architectural knowledge of data pipelines.
• Experience designing APIs and integrations.
• Experience with event-driven architectures.
• Experience processing structured and unstructured enterprise data.
• Experience designing data ingestion, transformation, retrieval, and processing workflows.
• Strong understanding of vector data management and retrieval.

• Strong enterprise architecture skills.
• Experience implementing security-by-design principles.
• Ability to design secure AI/ML architectures.
• Experience addressing data security, access controls, privacy, and compliance requirements.
• Ability to define architecture standards, patterns, and best practices for enterprise AI solutions.

• Experience working in the financial services industry preferred.
• Commercial real estate experience is highly desirable, particularly in Commercial Real Estate Credit, Loan Servicing, Asset Management.
• Ability to apply AI/ML solutions to financial services use cases and domain-specific data.

• Python
• Microsoft Azure
• Azure ML / Cloud ML Platforms
• Pinecone
• DB Vector
• Vector Databases
• Lang Chain
• Llama Index
• RAG
• LLMs
• Embeddings
• Fine-Tuning
• Vectorization
• Document AI
• NLP
• Knowledge Graphs
• Document Chunking
• Docker
• Kubernetes
• APIs
• Event-Driven Systems
• Data Pipelines

• AI Architecture
• Machine Learning
• Enterprise Architecture
• RAG
• LLMs
• Python
• Azure
• Vector Databases
• Pinecone
• Lang Chain / llamaIndex
• Fine-Tuning
• Embeddings
• Document AI
• NLP
• Unstructured Data Processing
• Data Pipelines
• API Architecture
• Event-Driven Architecture
• Docker / Kubernetes
• Security-by-Design

• Financial Services
• Commercial Real Estate
• Real Estate Credit
• Loan Servicing
• Asset Management
• Knowledge Graphs
• Advanced Vector Search
• Enterprise AI Platforms
• AI Governance
• Cloud Security