AI Architect

AI Architect
Atlanta, GA

Job Description

Roles & Responsibilities

Key Responsibilities

Enterprise AI Architecture

• Define end-to-end architecture for Generative AI, Agentic AI, machine learning, and intelligent automation solutions.
• Translate airline business priorities into scalable AI capabilities, reference architectures, solution patterns, and implementation roadmaps.
• Design reusable AI services across digital channels, airline operations, IT operations, customer service, engineering, and enterprise functions.
• Establish architecture standards for model integration, orchestration, data access, APIs, security, observability, evaluation, and deployment.
• Review solution designs and ensure alignment with enterprise standards and target architecture.

Generative AI and Agentic AI

• Architect enterprise-grade LLM solutions using RAG, knowledge grounding, tool integration, and multi-agent orchestration.
• Design autonomous and human-in-the-loop workflows with clear controls, approvals, escalations, and auditability.
• Define patterns for agent planning, reasoning, state management, memory, function calling, structured outputs, and secure tool access.
• Evaluate frameworks such as LangGraph, LangChain, Semantic Kernel, CrewAI, or equivalent enterprise technologies.
• Design reusable components for prompts, tools, workflows, model gateways, evaluation, guardrails, and agent observability.

Airline Business and Operational Solutions

• Partner with airline business and technology teams to identify and prioritize high-value AI opportunities.
• Architect solutions for digital customer experience, personalization, operational reliability, disruption management, reservations, customer service, employee assistance, major incident management, intelligent IT operations, engineering productivity, and knowledge management.
• Design AI capabilities for high-volume, near-real-time, customer-facing, and operationally critical environments.
• Balance innovation and speed with availability, reliability, safety, security, and operational stability.

Data, Context, and Knowledge Architecture

• Design secure data and knowledge architectures that ground AI solutions in trusted enterprise information.
• Define patterns for ingestion, chunking, metadata, embeddings, vector search, reranking, retrieval, response validation, and knowledge freshness.
• Integrate structured, unstructured, streaming, and operational data through enterprise data platforms, APIs, and event streams.
• Partner with data teams to ensure quality, lineage, access control, privacy, and governance for information used by AI systems.

AWS Cloud and AI Platforms

• Architect AI solutions using Amazon Bedrock, SageMaker, OpenSearch, S3, EKS/ECS, Lambda, Step Functions, API Gateway, Glue, Athena, Redshift, Kinesis/MSK, IAM, KMS, Secrets Manager, and CloudWatch.
• Evaluate models and services based on quality, security, latency, scalability, portability, reliability, and cost.
• Design cloud-native AI platforms that support experimentation, controlled de ployment, enterprise reuse, and model choice.
• Integrate third-party and open-source models where appropriate while maintaining enterprise security and governance.

AI Engineering and Integration

• Provide hands-on architecture guidance for Python-based AI services, APIs, microservices, and event-driven applications.
• Define secure integration patterns for agents to interact with enterprise applications, APIs, databases, and operational tools.
• Establish standards for schema validation, exception handling, retries, fallbacks, rate limits, and human escalation.
• Guide teams in building modular, testable, reusable, and maintainable AI components.

LLMOps, MLOps, Observability, and Production Readiness

• Define lifecycle standards for model selection, prompt management, training, fine-tuning, testing, deployment, monitoring, versioning, and retirement.
• Establish CI/CD and automated testing for models, prompts, retrieval pipelines, agents, APIs, and supporting services.
• Design evaluation frameworks covering accuracy, groundedness, relevance, safety, latency, reliability, operational impact, and cost.
• Implement end-to-end tracing and observability for model calls, retrieval decisions, agent execution, tool usage, and failures.
• Define production-readiness criteria, rollback approaches, service objectives, support models, and incident-response procedures.

Responsible AI, Security, and Governance

• Embed responsible AI, privacy, cybersecurity, compliance, and risk controls into architecture and delivery.
• Define identity, authorization, encryption, data isolation, auditability, secrets management, and sensitive-data protection controls.
• Implement guardrails for prompt injection, hallucination, data leakage, unsafe output, and unauthorized tool execution.
• Ensure traceability and appropriate human oversight for high-impact or operationally sensitive AI recommendations and actions.

Technical Leadership and Collaboration

• Serve as a trusted AI architecture advisor to business and technology leadership.
• Lead architecture reviews, technical workshops, design sessions, and AI use-case assessments.
• Provide clear recommendations on technology selection, implementation approach, risks, dependencies, and trade-offs.
• Mentor AI engineers, data scientists, software engineers, and solution architects.
• Create reference architectures, reusable patterns, standards, playbooks, and contributions to the enterprise AI roadmap.