Advanced AI Software Engineer

Honeywell•Phoenix, AZ

3h•Hybrid

About The Position

As an Advanced AI Software Engineer at Honeywell, you will design, develop, deploy, and own advanced AI/ML and Generative AI capabilities that power enterprise software applications and products. You will apply deep expertise in Large Language Models (LLMs), Generative AI, agentic AI, machine learning, and software engineering to build secure, scalable, reliable, and production-ready AI solutions. You will provide technical leadership across the AI software development lifecycle, from architecture and experimentation through evaluation, deployment, observability, optimization, and production support. You will collaborate closely with software, product, data, cloud/platform, cybersecurity, and architecture teams to translate complex business requirements into robust AI-powered solutions.

Requirements

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.

Nice To Haves

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.

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.