Advanced Software Engr

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    "ShortDescriptionStr": "As an Advanced Software Engineer here at Honeywell, you will lead advanced software development projects, enhance and maintain critical software components, and troubleshoot complex issues. Your expertise will optimize software performance and drive innovation. You will report directly to our Sr. Engineering Manager and you’ll work out of our Fort Washington PA location on a hybrid work schedule.",
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    "ExternalDescriptionStr": "<p>The <strong>Advanced Software Engineer</strong> – is a senior technical contributor responsible for designing, developing, and maintaining high‑quality, scalable software solutions that leverage modern software engineering practices with AI‑enabled capabilities.</p>\n<p>This role goes beyond traditional software development by integrating AI‑assisted workflows, machine learning models, and GenAI technologies into Honeywell software products, platforms, and engineering processes. The engineer will work across the full software lifecycle: requirement, architecture, design, implementation, testing, deployment, and operational support while collaborating with cross‑functional teams to deliver reliable, secure, and maintainable systems used in mission‑critical environments.</p>\n<p>&nbsp;</p>\n<p>This position is based in Fort Washington PA.</p>\n<p>&nbsp;</p>\n<p><strong>BENEFITS OF WORKING FOR HONEYWELL</strong></p>\n<p>In addition to a competitive salary, leading-edge work, and developing solutions side-by-side with dedicated experts in their fields, Honeywell employees are eligible for a comprehensive benefits package. This package includes employer subsidized Medical, Dental, Vision, and Life Insurance; Short-Term and Long-Term Disability; 401(k) match, Flexible Spending Accounts, Health Savings Accounts, EAP, and Educational Assistance; Parental Leave, Paid Time Off (for vacation, personal business, sick time, and parental leave), and 12 Paid Holidays .For more Honeywell Benefits information visit: https://benefits.honeywell.com/</p>\n<p>The application period for the job is estimated to be 40 days from the job posting date; however, this may be shortened or extended depending on business needs and the availability of qualified candidates</p>\n<p>Job Posting Date: 04/08/2026</p>\n<p style=\"margin-left:0.25in\">&nbsp;</p>",
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    "CorporateDescriptionStr": "<span>Honeywell helps organizations solve the world's most complex challenges in automation, the future of aviation and energy transition. As a trusted partner, we provide actionable solutions and innovation through our Aerospace Technologies, Building Automation, Energy and Sustainability Solutions, and Industrial Automation business segments – powered by our Honeywell Forge software – that help make the world smarter, safer and more sustainable.</span>",
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    "ExternalQualificationsStr": "<p> Basic Qualifications <span style=\"color:#ee0000\"> </span></p><ul><li>5+ years of professional software engineering experience.</li><li>Prior experience integrating AI or data‑driven components into software products.</li><li>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).</li><li>Experience building and maintaining production‑grade software systems, including containerized and orchestrated environments using Docker and Kubernetes.</li></ul><p style=\"margin-left:0.25in\"> </p><p><strong>Preferred Qualifications</strong></p><ul><li>Master’s  or Bachelor’s degree in Computer Science, Software Engineering, Data Science, or a related technical field.</li><li>Experience in industrial, embedded, real‑time, or mission‑critical software environments.  <span style=\"color:#ee0000\"> </span></li><li>Familiarity with cloud platforms, distributed systems, or microservices architectures.</li><li>Experience with machine learning fundamentals, including model types, evaluation metrics, and data considerations. </li><li>Familiarity with Generative AI concepts, such as large language models (LLMs), small language models (SLMs), embeddings, prompt engineering, and retrieval‑augmented generation (RAG). </li><li>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). </li><li>Exposure to MLOps practices, including experiment tracking, model versioning, and automated deployment pipelines. </li><li>Experience with cloud‑based AI platforms (e.g., Azure ML, Databricks, Vertex AI, or equivalent).</li></ul><p> </p>",
    "InternalQualificationsStr": "<p> Basic Qualifications <span style=\"color:#ee0000\"> </span></p><ul><li>5+ years of professional software engineering experience.</li><li>Prior experience integrating AI or data‑driven components into software products.</li><li>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).</li><li>Experience building and maintaining production‑grade software systems, including containerized and orchestrated environments using Docker and Kubernetes.</li></ul><p style=\"margin-left:0.25in\"> </p><p><strong>Preferred Qualifications</strong></p><ul><li>Master’s  or Bachelor’s degree in Computer Science, Software Engineering, Data Science, or a related technical field.</li><li>Experience in industrial, embedded, real‑time, or mission‑critical software environments.  <span style=\"color:#ee0000\"> </span></li><li>Familiarity with cloud platforms, distributed systems, or microservices architectures.</li><li>Experience with machine learning fundamentals, including model types, evaluation metrics, and data considerations. </li><li>Familiarity with Generative AI concepts, such as large language models (LLMs), small language models (SLMs), embeddings, prompt engineering, and retrieval‑augmented generation (RAG). </li><li>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). </li><li>Exposure to MLOps practices, including experiment tracking, model versioning, and automated deployment pipelines. </li><li>Experience with cloud‑based AI platforms (e.g., Azure ML, Databricks, Vertex AI, or equivalent).</li></ul><p> </p>",
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    "ExternalResponsibilitiesStr": "<p><strong>Key Responsibilities</strong></p><p>Advanced Software Engineering</p><ul><li>Design, develop, test, and maintain complex software systems using modern programming languages, frameworks, and architectural patterns.</li><li>Own features or subsystems end‑to‑end, from requirements and design through deployment and long‑term support.</li><li>Apply disciplined software development practices including version control, code reviews, automated testing, and documentation.</li><li>Ensure software meets Honeywell standards for quality, reliability, performance, cybersecurity, and safety where applicable.</li><li>Diagnose and resolve complex technical issues in development and production environments.</li></ul><p>AI‑Enabled Software Development</p><ul><li>Integrate AI‑driven capabilities into software products and internal engineering tools to improve functionality, productivity, and decision‑making.</li><li>Apply AI techniques for use cases such as intelligent automation, anomaly detection, predictive insights, natural‑language interfaces, and engineering workflow acceleration.</li><li>Collaborate with data scientists and platform teams to incorporate machine learning or GenAI components into production‑grade software systems.</li></ul><p>GenAI &amp; Applied AI Usage</p><ul><li>Identify and evaluate high‑value opportunities to apply GenAI within software products and engineering processes.</li><li>Use GenAI tools responsibly to assist with code generation, documentation, test creation, debugging, analysis, and summarization.</li><li>Design software interfaces and workflows that safely and effectively consume AI model outputs.</li><li>Validate AI‑assisted outputs to ensure correctness, robustness, and alignment with Honeywell standards.</li></ul><p>Model Integration &amp; MLOps </p><ul><li>Integrate trained ML models into applications or services using APIs or embedded inference.</li><li>Participate in or support model lifecycle workflows including training, validation, deployment, and monitoring in collaboration with AI/ML teams.</li><li>Apply MLOps principles such as CI/CD for models, versioning, environment promotion, and observability.</li></ul><p>Technical Leadership &amp; Collaboration</p><ul><li>Act as a technical mentor for less‑experienced engineers and contribute to team engineering best practices.</li><li>Participate in architecture and design reviews, providing guidance on scalability, maintainability, and AI integration.</li><li>Work closely with systems, hardware, cybersecurity, product management, and test teams across Honeywell.</li></ul>",
    "InternalResponsibilitiesStr": "<p><strong>Key Responsibilities</strong></p><p>Advanced Software Engineering</p><ul><li>Design, develop, test, and maintain complex software systems using modern programming languages, frameworks, and architectural patterns.</li><li>Own features or subsystems end‑to‑end, from requirements and design through deployment and long‑term support.</li><li>Apply disciplined software development practices including version control, code reviews, automated testing, and documentation.</li><li>Ensure software meets Honeywell standards for quality, reliability, performance, cybersecurity, and safety where applicable.</li><li>Diagnose and resolve complex technical issues in development and production environments.</li></ul><p>AI‑Enabled Software Development</p><ul><li>Integrate AI‑driven capabilities into software products and internal engineering tools to improve functionality, productivity, and decision‑making.</li><li>Apply AI techniques for use cases such as intelligent automation, anomaly detection, predictive insights, natural‑language interfaces, and engineering workflow acceleration.</li><li>Collaborate with data scientists and platform teams to incorporate machine learning or GenAI components into production‑grade software systems.</li></ul><p>GenAI &amp; Applied AI Usage</p><ul><li>Identify and evaluate high‑value opportunities to apply GenAI within software products and engineering processes.</li><li>Use GenAI tools responsibly to assist with code generation, documentation, test creation, debugging, analysis, and summarization.</li><li>Design software interfaces and workflows that safely and effectively consume AI model outputs.</li><li>Validate AI‑assisted outputs to ensure correctness, robustness, and alignment with Honeywell standards.</li></ul><p>Model Integration &amp; MLOps </p><ul><li>Integrate trained ML models into applications or services using APIs or embedded inference.</li><li>Participate in or support model lifecycle workflows including training, validation, deployment, and monitoring in collaboration with AI/ML teams.</li><li>Apply MLOps principles such as CI/CD for models, versioning, environment promotion, and observability.</li></ul><p>Technical Leadership &amp; Collaboration</p><ul><li>Act as a technical mentor for less‑experienced engineers and contribute to team engineering best practices.</li><li>Participate in architecture and design reviews, providing guidance on scalability, maintainability, and AI integration.</li><li>Work closely with systems, hardware, cybersecurity, product management, and test teams across Honeywell.</li></ul>",
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