AI Engr II

Skills this role screens for

About the role

Honeywell is hiring for the role of AI Engr II!

The work

Responsibilities of the Candidate

• Design and develop production-grade Generative AI and Agentic AI applications.
• Build autonomous and semi-autonomous AI agents that can reason, plan, use tools, and collaborate with other agents.
• Develop advanced RAG pipelines leveraging vector databases, embeddings, and enterprise data sources.
• Create multi-step reasoning workflows combining deterministic business logic with LLM-generated reasoning.
• Implement short-term and long-term memory architectures for persistent contextual interactions.
• Design and maintain resilient AI workflows incorporating self-correction, failure recovery, debugging loops, and Human-in-the-Loop (HITL) controls.
• Integrate LLMs, VLMs, enterprise APIs, databases, and external tools into agent ecosystems.
• Deploy and manage AI applications across cloud, hybrid, and on-premises environments.
• Collaborate with product, engineering, and domain experts to translate business requirements into scalable AI solutions.
• Ensure production readiness through monitoring, testing, performance optimization, and governance controls.

Who they want

Requirements

• Strong proficiency in Python.
• Experience designing and implementing enterprise-grade software solutions.
• Familiarity with software engineering best practices, version control, testing, and CI/CD pipelines.
• Hands-on production experience with agent orchestration frameworks such as LangGraph, OpenAI Agents SDK, or equivalent.
• Deep understanding of foundational agentic design patterns, including:
• Reflection
• Tool Use
• Planning
• Multi-Agent Collaboration
• Proven ability to develop:
• Self-healing workflows
• Automated debugging mechanisms
• Human-in-the-Loop approval processes
• Quality assurance and audit checkpoints
• Experience applying data mining and machine learning techniques, including:
• Classification
• Regression
• Clustering
• Decision Trees
• Neural Networks
• Support Vector Machines (SVM)
• Anomaly Detection
• Recommender Systems
• Pattern Discovery
• Text Mining
• Knowledge of statistical modeling and predictive analytics.
• Experience delivering AI solutions in regulated enterprise environments.
• Familiarity with AI governance, security, privacy, and responsible AI practices.
• Experience building multi-agent ecosystems for operational or business workflows.
• Understanding of model evaluation, observability, and AI performance monitoring.
• Exposure to MLOps and operationalization of AI workloads.
• Bachelor's or master's degree in computer science, Artificial Intelligence, Machine Learning, Data Science, or a related technical discipline.