Honeywell has 74 active AI-related job listings. The company is primarily hiring for roles related to agents, which constitute 30% of the openings, and serving infrastructure, making up 26%. Engineering is the dominant function, with 73 roles. The majority of these positions are located in India, with 49 listings, followed by the United States with 16. Frequent technical tags include model serving, agent orchestration, and RAG. In the last 30 days, Honeywell has posted 43 new AI roles, representing a 79% increase compared to the previous 30-day period.
Currently tracking 49 active AI roles, with 162 new openings in the last 4 weeks. Primary focus: Serve · Engineering. Salary range $99k–$124k (avg $112k).
Honeywell currently has 70 active AI-related roles in our index. The most common open titles are: Advanced Software Engr (5), Advanced AI Engr (4), Software Engr II (4), AI Engr II (3), AI Manager (3). Most positions are in Engineering and Product.
Honeywell's active AI hiring is concentrated in: agents (33%), application (24%), serving infrastructure (21%). These categories follow a seven-stage AI lifecycle: data, pre-training, post-training, serving infrastructure, agents, evaluation, and application.
Honeywell is hiring AI talent in: India (45 roles), United States (17 roles), Mexico (2 roles), China (2 roles).
Job postings at Honeywell most frequently reference: model serving, agent orchestration, rag, inference infra, llm observability.
In the past 30 days, Honeywell has posted 45 new AI-related roles. That is a +36% change versus the prior 30 days (33 → 45).
| Title | Stage | AI score |
|---|---|---|
| AI Engr II Develops and implements AI algorithms, models, and systems, collaborating with cross-functional teams to integrate AI solutions into existing systems and processes, ensuring scalability, efficiency, and robustness. Focuses on refining AI models and improving performance through experimentation and analysis. | Post-train | 7 |
| AI Engineer II AI Engineer II at Honeywell, responsible for developing and implementing AI algorithms, models, and systems to solve complex business problems and integrating them into existing systems. Focuses on refining AI models through experimentation and analysis. | Post-train | 7 |