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.
• 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.

Qualifications

• 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).