Sr AI Developer

Sr AI Software Developer 

As a Sr AI Software Developer 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 or other Aerospace locations on a hybrid work schedule.

Relocation support may be provided. 

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.