AI Architecture

American IT Systems•Dallas, TX

4d•Hybrid

About The Position

We are seeking a seasoned AI Architect who combines deep technical expertise with the ability to articulate and sell AI solutions to enterprise stakeholders. This is a hybrid role requiring both architectural rigor and strong communication skills—someone who can inspire confidence in the boardroom and lead engineering teams in the field. Critically, we are looking for someone with genuine hands-on implementation experience—an architect who not only designs AI solutions but has actually built and deployed them end-to-end. We value the full journey: from initial discovery and solution design, through proof-of-concept, to production deployment and ongoing optimization. The ideal candidate brings fluency across generative AI, agentic AI, and traditional machine learning, with enterprise depth in defining architectures that span orchestration layers, model selection, data integration, and governance. They understand AI both as a disruptive force—enabling entirely new business models—and as an infusion capability, embedding intelligence into existing enterprise systems and workflows.

Requirements

8+ years in enterprise software architecture, with at least 3+ years focused on AI/ML solutions at scale
Deep knowledge of generative AI, agentic AI systems, and traditional machine learning, including:
Large Language Models (LLMs) – selection, fine-tuning, prompt engineering, and lifecycle management
Agentic AI design – agent orchestration, tool use, multi-agent coordination, and MCP server configuration
RAG (Retrieval-Augmented Generation) – vector databases, embedding models, document grounding, and hybrid search
Traditional ML – supervised/unsupervised learning, model training pipelines, and MLOps practices
Hands-on multi-model experience—selecting, integrating, and orchestrating frontier and open-source models including:
Anthropic Claude (e.g., Claude Sonnet, Claude Opus) for reasoning and complex language tasks
OpenAI GPT series for generative and agentic use cases
Google Gemini for multimodal and large context window applications
Meta Llama and other open-source models for on-premise or cost-optimized deployments
Ability to benchmark, evaluate, and select the right model for the right task in a production context
Proven experience with major cloud AI platforms (AWS Bedrock, Azure OpenAI, Google Vertex AI) and associated infrastructure services
Experience integrating AI solutions with enterprise platforms such as Salesforce, SAP, ServiceNow, Microsoft 365, or similar
Demonstrated track record of successfully translating AI architectures into delivered, production-grade solutions
Strong pre-sales capability—experience leading solution workshops, responding to RFPs, and presenting to senior decision-makers
Enterprise AI architecture patterns and multi-layered solution design
Agent orchestration frameworks (e.g., LangGraph, AutoGen, CrewAI, watsonx Orchestrate) and MCP protocol-based integrations
Prompt engineering, chain-of-thought reasoning, and advanced LLM interaction patterns
Vector databases (e.g., Pinecone, Weaviate, pgvector, Chroma) and semantic search architecture
Cloud-native and hybrid deployment architectures across AWS, Azure, GCP, and on-premise environments
API design, microservices, event-driven architecture, and enterprise system integration patterns
AI governance, responsible AI frameworks, model risk management, bias detection, and explainability
AI observability and monitoring—model drift detection, performance tracking, data quality, and inference health
Data architecture, data pipelines, and governance in the context of AI workloads
Security and compliance considerations for enterprise AI deployments
Exceptional communication and presentation skills—able to engage C-suite executives, business stakeholders, and deep technical teams with equal confidence
Strong sales acumen and solution storytelling ability—can craft a compelling narrative around AI value and guide customers through complex solution decisions
Strategic thinker with a consistent focus on business outcomes over technical novelty
Proven ability to lead through influence in matrixed enterprise environments without direct authority
Comfortable navigating ambiguity and driving clarity—from early-stage concept to production delivery
Confident public speaker—comfortable presenting at customer briefings, industry events, and executive forums
Collaborative and empathetic leader—able to mentor, inspire, and upskill diverse technical teams

Nice To Haves

Relevant AI/cloud certifications (AWS Certified Machine Learning, Azure AI Engineer, Google Professional ML Engineer, or equivalent)
Experience with AI governance platforms and model observability tooling (e.g., Fiddler, Arize, Arthur AI, or similar)
Background in enterprise consulting, solution architecture, or pre-sales engineering roles
Published thought leadership—blog posts, whitepapers, conference presentations, or open-source contributions in the AI space
Experience scaling AI solutions across multiple business units or global enterprise environments
Exposure to regulated industries (financial services, healthcare, government) where AI governance and compliance are mission-critical
Familiarity with AI safety, alignment principles, and emerging regulatory frameworks (EU AI Act, NIST AI RMF)

Responsibilities

Design and define enterprise-scale AI architectures, integrating AI through both disruptive innovation and intelligent infusion into existing business systems and platforms
Architect multi-layered AI solutions spanning agent orchestration frameworks, MCP (Model Context Protocol) servers, LLM integrations, RAG pipelines, and supporting data infrastructure
Evaluate and select appropriate AI technologies, models, frameworks, and cloud services based on business requirements, cost, and governance constraints
Lead pre-sales and solution design engagements—translating complex AI concepts into compelling business value propositions for executive and technical audiences
Develop solution strategies with clear ROI narratives; actively participate in customer-facing presentations, RFP responses, and solution workshops
Own the complete solution lifecycle from whiteboard to production—providing architectural leadership through design, development, testing, and deployment phases
Guide and support implementation teams with hands-on experience, solving real-world engineering challenges as they arise in production environments
Define standards, patterns, and best practices for AI architecture across the organization and with external partners
Mentor and upskill technical teams in agentic AI design, multi-model integration, prompt engineering, and responsible AI practices
Stay current with the rapidly evolving AI landscape—evaluating emerging models, tools, and frameworks and advising on adoption strategy