Responsibilities
- Design and deliver scalable AI/ML systems, including data pipelines, model lifecycle management, GenAI, agentic AI, and inference services.
- Direct AI and agentic AI architecture strategy, including multi-agent frameworks, orchestration, reasoning pipelines, and governance.
- Define buy-versus-build-versus-tune strategies for open-source and foundation models.
- Lead base-model, fine-tuning, and RAG approaches for building automation use cases.
- Plan and launch AI platforms that meet scalability, reliability, security, safety, responsible AI, and regulatory requirements.
- Set architecture standards and best practices for AI/ML platforms, LLM/GenAI, and agentic AI ecosystems.
- Define AI/ML opportunities and convert them into actionable engineering plans with product, data science, and business leaders.
- Advise teams on model selection, training, feature engineering, and MLOps.
- Assess and adopt AI technologies, frameworks, and tools.
- Lead complex AI architecture programs and mentor architects, engineers, and planners.
Requirements
- Bachelor’s or master’s degree in computer science, engineering, AI, data science, or a related field.
- 14+ years of overall experience, including 5 or more years of experience in AI architecture.
- Experience with AI frameworks and tools such as TensorFlow or PyTorch.
- Proven experience architecting machine learning, LLM/GenAI, and autonomous multi-agent solutions using frameworks such as LangGraph.
- Deep knowledge of transformer architecture, context-window management, model optimization, SFT, PEFT, fine-tuning, and token budgeting for models such as GPT-4, Llama 3, Phi, and Mistral 7B.
- Proficiency with LangChain, Semantic Kernel, or LangGraph for stateful multi-agent orchestration.
- Proficiency modeling industrial hierarchies as knowledge graphs for structured LLM context.
- Ability to design multimodal RAG pipelines using vision-language models and engineering documentation.
- Experience establishing AI governance, safety measures, ethical guidelines, and protections against hallucinations and adversarial prompts.
- Proven leadership, mentoring, stakeholder relationship-building, and large-scale project leadership skills.
- Deep understanding of cloud-native architecture, preferably Azure, security frameworks, data platforms, and modern AI/ML deployment patterns.
- Experience with agentic AI frameworks, vector databases, orchestration layers, autonomous workflow systems, data monetization, secure data sharing, and analytics in hybrid platforms.
Categories
About Honeywell
Honeywell Technologies is a global, pure-play automation company with a legacy of innovating to help solve the world’s most mission-critical challenges, enhancing the quality of life for people and communities around the world. We serve the building, industrial, and process sectors with a broad portfolio of services, solutions, and products, underpinned by our Honeywell Technologies Accelerator operating system and Honeywell Technologies Forge intelligence layer. By combining the deep domain expertise of our more than 50,000 employees with decades of data from our global installed base, we are uniquely positioned to lead the industrial sector’s transition from automation to autonomy. For additional information on how Honeywell processes your personal information please visit https://www.honeywell.com/privacy-statement.