Advanced Industrial Software Engineer

Responsibilities

• Design, develop, test, and maintain complex software systems using modern programming languages, frameworks, and architectural patterns.
• Own features or subsystems end‑to‑end, from requirements and design through deployment and long‑term support.
• Apply disciplined software development practices including version control, code reviews, automated testing, and documentation.
• Ensure software meets Honeywell standards for quality, reliability, performance, cybersecurity, and safety where applicable.
• Diagnose and resolve complex technical issues in development and production environments.
• Integrate AI‑driven capabilities into software products and internal engineering tools to improve functionality, productivity, and decision‑making.
• Apply AI techniques for use cases such as intelligent automation, anomaly detection, predictive insights, natural‑language interfaces, and engineering workflow acceleration.
• Collaborate with data scientists and platform teams to incorporate machine learning or GenAI components into production‑grade software systems.
• Identify and evaluate high‑value opportunities to apply GenAI within software products and engineering processes.
• Use GenAI tools responsibly to assist with code generation, documentation, test creation, debugging, analysis, and summarization.
• Design software interfaces and workflows that safely and effectively consume AI model outputs.
• Validate AI‑assisted outputs to ensure correctness, robustness, and alignment with Honeywell standards.
• Act as a technical mentor for less‑experienced engineers and contribute to team engineering best practices.
• Participate in architecture and design reviews, providing guidance on scalability, maintainability, and AI integration.
• Work closely with systems, hardware, cybersecurity, product management, and test teams across Honeywell.

• Bachelor’s degree in Computer Science, Software Engineering, Data Science, or a related technical field.
• Minimum of 5 years of professional software engineering experience in the industrial field.
• Prior experience integrating AI or data‑driven components into software products.
• Strong proficiency in one or more modern programming languages or frameworks (e.g., C++, C#, Java, Python, or modern web technologies such as HTML/React).
• Experience building and maintaining production‑grade software systems, including containerized and orchestrated environments using Docker and Kubernetes.

• Experience in industrial, embedded, real‑time, or mission‑critical software environments.
• Familiarity with cloud platforms, distributed systems, or microservices architectures.
• Experience with machine learning fundamentals, including model types, evaluation metrics, and data considerations.
• Familiarity with Generative AI concepts, such as large language models (LLMs), small language models (SLMs), embeddings, prompt engineering, and retrieval‑augmented generation (RAG).
• Experience working with high‑performance artificial intelligence technologies, including leading commercial and open‑source models and inference frameworks (e.g., LLMs, vision models, local or edge inference runtimes).
• Experience with cloud‑based AI platforms (e.g., Azure ML, Databricks, Vertex AI, or equivalent).

Demonstrates expertise in software engineering with a focus on integrating AI‑driven capabilities and maintaining production‑grade systems. Proficient in modern programming languages and frameworks, with a strong emphasis on quality, reliability, and cybersecurity standards.

• Docker
• Kubernetes
• Azure ML
• Databricks
• Vertex AI