Intelligence and National Security Alliance
Artificial Intelligence Specialist
Intelligence and National Security Alliance, Chicago, Illinois, United States, 60290
AI Architect
A highly experienced AI Architect with 10–12 years of professional experience in designing, building, and scaling enterprise-grade AI and Generative AI (GenAI) solutions across leading public cloud platforms (Azure, AWS, GCP). The ideal candidate will play a strategic role in AI solution architecture, data & model engineering, cloud integration, and MLOps/LLMOps frameworks, while working closely with business and technology teams to drive measurable outcomes through AI innovation.
Key Responsibilities
AI Solution Architecture:
Enterprise Integration:
GenAI Solution Design:
MLOps & LLMOps:
Data Architecture Collaboration:
Stakeholder Engagement:
Innovation & Research:
Performance Optimization:
Mentorship:
Technical Skills & Competencies
AI/ML Frameworks: TensorFlow, PyTorch, Scikit-learn, Hugging Face, Lang Chain, LlamaIndex, Open AI API, etc.
GenAI & LLMs: Experience with GPT-4/4o, Claude, Gemini, Mistral, LLaMA, fine-tuning, RAG (Retrieval-Augmented Generation), and prompt engineering.
Cloud Platforms: Deep understanding of Azure AI (OpenAI, AI Search, ML), AWS (SageMaker, Bedrock, Comprehend, Lex), GCP Vertex AI.
MLOps & LLMOps: Azure ML Pipelines, Sage Maker Pipelines, MLflow, Kubeflow, or custom CI/CD for AI.
Data & Integration: Experience with Data Lakes, Databricks, Synapse, Kafka, REST/GraphQL APIs, and vector DBs like Pinecone, We aviate, FAISS, Milvus, or Cosmos DB.
Programming: Python, SQL, REST APIs, YAML, Docker, Kubernetes, Terraform (IaC).
Responsible AI: Understanding of bias detection, model explain ability, and governance principles.
Architecture Design: Expertise in TOGAF/Well-Architected frameworks and AI reference architectures.
Qualifications
Proven experience in designing and delivering AI solutions at enterprise scale.
Cloud certifications (Azure AI Engineer, AWS ML Specialty, or GCP ML Engineer) preferred.
Strong communication and presentation skills for stakeholder engagement and executive alignment.
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Key Responsibilities
AI Solution Architecture:
Enterprise Integration:
GenAI Solution Design:
MLOps & LLMOps:
Data Architecture Collaboration:
Stakeholder Engagement:
Innovation & Research:
Performance Optimization:
Mentorship:
Technical Skills & Competencies
AI/ML Frameworks: TensorFlow, PyTorch, Scikit-learn, Hugging Face, Lang Chain, LlamaIndex, Open AI API, etc.
GenAI & LLMs: Experience with GPT-4/4o, Claude, Gemini, Mistral, LLaMA, fine-tuning, RAG (Retrieval-Augmented Generation), and prompt engineering.
Cloud Platforms: Deep understanding of Azure AI (OpenAI, AI Search, ML), AWS (SageMaker, Bedrock, Comprehend, Lex), GCP Vertex AI.
MLOps & LLMOps: Azure ML Pipelines, Sage Maker Pipelines, MLflow, Kubeflow, or custom CI/CD for AI.
Data & Integration: Experience with Data Lakes, Databricks, Synapse, Kafka, REST/GraphQL APIs, and vector DBs like Pinecone, We aviate, FAISS, Milvus, or Cosmos DB.
Programming: Python, SQL, REST APIs, YAML, Docker, Kubernetes, Terraform (IaC).
Responsible AI: Understanding of bias detection, model explain ability, and governance principles.
Architecture Design: Expertise in TOGAF/Well-Architected frameworks and AI reference architectures.
Qualifications
Proven experience in designing and delivering AI solutions at enterprise scale.
Cloud certifications (Azure AI Engineer, AWS ML Specialty, or GCP ML Engineer) preferred.
Strong communication and presentation skills for stakeholder engagement and executive alignment.
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