Advanced AI Software Engineer

Qualifications

• YOU MUST HAVE
• 4+ years of hands-on AI/ML engineering experience, including designing, developing, deploying, and supporting AI/ML and Generative AI solutions in production environments.
• 4+ years of hands-on experience with modern AI/ML technologies, including Large Language Models (LLMs), Generative AI, agentic AI prompt/context engineering, embeddings, vector databases, semantic/hybrid search, Transformers, and AI/ML frameworks.
• 4+ years of experience architecting and delivering production AI/ML applications, including APIs/microservices, cloud deployment, model/LLM evaluation, automated testing, observability, performance optimization, security, scalability, and cross-functional technical delivery.
• 5+ years of software engineering/programming experience, with strong hands-on proficiency in Python and experience with at least one additional programming language such as C++ or Java, developing scalable, maintainable, production-quality software.
• WE VALUE
• Bachelor’s or Master’s degree in Computer Science, Artificial Intelligence, Machine Learning, or a related technical field.
• Advanced experience with AI/ML frameworks and ecosystems such as PyTorch, TensorFlow, Hugging Face, LangChain, Semantic Kernel, or equivalent technologies.
• Experience with enterprise-grade vector databases and search technologies such as Azure AI Search, Elasticsearch, PostgreSQL/pgvector, Pinecone, or equivalent technologies.
• Experience with Azure AI services/Azure AI Foundry and/or Google Gemini/Vertex AI and cloud-based AI infrastructure.
• Experience with containerization and orchestration technologies such as Docker and Kubernetes.
• Experience building automated deployment pipelines using CI/CD, DevOps, Infrastructure as Code, and MLOps/LLMOps practices.
• Experience with AI observability, model/application telemetry, prompt and model versioning, evaluation pipelines, and production quality monitoring.
• Knowledge of Responsible AI, AI security, data privacy, model risk, prompt-injection mitigation, content safety, and enterprise AI governance.
• BENEFITS OF WORKING FOR HONEYWELL
• 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. Learn more (https://benefits.honeywell.com/)
• 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. Job Posted: October 14, 2026

About Honeywell Technologies

Key Responsibilities

• Architect, design, develop, and deploy production-grade applications and software platforms using Large Language Models (LLMs), Generative AI, and agentic AI technologies.
• Design and implement advanced architectures, prompt and context-management strategies, embeddings, vector databases, semantic/hybrid search, reranking, and knowledge-retrieval pipelines.
• Build AI agents and multi-step AI workflows, including tool/function calling, orchestration, memory/context management, and integration with enterprise applications and data sources.
• Optimize AI workloads for latency, throughput, scalability, token consumption, infrastructure utilization, and cost.
• Design and implement cloud-native AI solutions and integrate AI services with existing enterprise applications, APIs, data platforms, and software architectures.
• Provide technical leadership in architecture reviews, design reviews, code reviews, technical trade-off analysis, and engineering best practices.
• Drive engineering standards for software quality, automated testing, CI/CD, security, scalability, resiliency, maintainability, and operational readiness.
• Collaborate with product and engineering teams to translate business problems into technically feasible AI solutions and measurable outcomes.
• Mentor engineers and provide technical guidance on AI/ML architecture, software engineering practices, troubleshooting, and production deployment.