About the role

What will you do at Honeywell Technologies?

As a Senior Advanced AI Engineer here at Honeywell, you will play a crucial role

in the development of advanced AI solutions that drive business insights,

enhance decision-making processes and empower AI solutions. Your expertise will

help in critical data science development activities across all AI modalities

(classic, Gen and agentic) and data types (structured and unstructured).

You will report directly to our AI Director, and you’ll work out of our Phoenix,

AZ or Charlotte, NC location on a hybrid work schedule.

KEY RESPONSIBILITIES

* Design and develop AI application services and middleware that connect

classic ML models, GenAI/LLM systems, and agentic AI components to enterprise

applications and workflows.

* Build production‑grade RAG (Retrieval-Augmented Generation) services,

including chunking pipelines, embedding APIs, retrieval endpoints, caching,

re-ranking, and content policy enforcement.

* Develop agent tool adapters and integration layers enabling AI agents to

safely perform actions (e.g., Snowflake queries, workflow triggers, system

updates) using secure, controlled APIs.

* Implement policy, safety, and guardrail middleware that enforces PII

protection, content moderation, compliance rules, and safe function execution

for agentic systems.

* Create event‑driven and asynchronous services using AWS-native capabilities

for agent orchestration, callbacks, monitoring, and workflow routing.

* Build microservices and SDKs that enable scalable, low‑latency interactions

between AI models, vector databases, and enterprise systems.

* Collaborate with AI Architects, Platform Engineers, MLOps, Data Engineers,

and Data Scientists to ensure systems are reliable, secure, observable, and

aligned with best practices.

* Implement robust testing frameworks for AI-driven services including

regression tests, guardrail tests, prompt and agent behavior evaluations, and

functional correctness checks.

* Participate in code reviews, architectural discussions, and continuous

improvement initiatives to enhance the performance and reliability of

AI-powered applications.

Which skills does this role require?

PythonFastAPINode.jsTypeScriptGenAILLMRAGVector databasesLangGraphLangChainCloud-native servicesMLOpsData scienceAgentic AIAWSMicroservicesArtificial IntelligenceMachine LearningGenerative AILarge Language ModelsRetrieval-Augmented GenerationVector DatabasesSoftware EngineeringData ScienceCloud-nativeMiddlewareAPI IntegrationPrompt EngineeringObservabilityCI/CDAerospaceDefenseAutomationSystem ArchitectureGovernanceSecuritySnowflakeLLMs

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