Job Description
We are seeking a highly skilled AI Engineer II to design, build, deploy, and scale next-generation Generative AI and Agentic AI solutions for enterprise applications. The ideal candidate will have deep expertise in Large Language Models (LLMs), Retrieval Augmented Generation (RAG), agentic frameworks, and AI-driven workflow automation. This role will contribute to the development of intelligent AI agents capable of reasoning, planning, collaborating, and operating within complex enterprise environments.
Your role will also include overseeing, supervising and reviewing tasks performed by team members to ensure effective execution of work; managing end‑to‑end processes and projects for both internal and external clients with responsibility for timely and accurate delivery; issuing clear instructions and directions to team members on tasks to be performed; and mentoring and guiding junior colleagues to support their skill development, professional growth, and overall success
ResponsibilitiesKey Responsibilities
- 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.
- Ensure production readiness through monitoring, testing, performance optimization, and governance controls.
Required Technical Skills & Experience
Programming & Software Engineering
- Strong proficiency in Python.
- Experience designing and implementing enterprise-grade software solutions.
- Familiarity with software engineering best practices, version control, MLOps
Generative AI & Agentic AI
- 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
- Practical experience with:
- Function calling
- Structured outputs
- Prompt engineering
- Context window management
LLMs & Foundation Models
- Experience building applications using:
- Large Language Models (LLMs)
- Vision Language Models (VLMs)
- Embedding models
- Strong understanding of model capabilities, limitations, and optimization techniques.
AI Workflow Orchestration
- Proven ability to develop:
- Self-healing workflows
- Automated debugging mechanisms
- Human-in-the-Loop approval processes
- Quality assurance and audit checkpoints
Cloud & Deployment
- Experience deploying Generative AI solutions in:
- On-premises environments
- Hybrid architectures
- Public cloud environments
- Hands-on experience in any one of the below cloud platforms:
- Microsoft Azure
- AWS
- Google Cloud Platform (GCP)
Data Science & Machine Learning
- Experience applying data mining and machine learning techniques, including:
- Decision Trees
- Neural Networks
- Support Vector Machines (SVM)
- Anomaly Detection
- Recommender Systems
- Pattern Discovery
- Text Mining
YOU MUST HAVE
- 3-6 years experience
Skills Required
- 3-6 years of professional experience
- Strong proficiency in Python
- Experience designing and implementing enterprise-grade software solutions
- Familiarity with software engineering best practices, version control, and MLOps
- Production experience with agent orchestration frameworks such as LangGraph or OpenAI Agents SDK
- Understanding of agentic design patterns including reflection, tool use, planning, and multi-agent collaboration
- Experience with function calling, structured outputs, prompt engineering, and context window management
- Experience building applications with LLMs, VLMs, and embedding models
- Experience developing self-healing workflows, automated debugging, human-in-the-loop approvals, quality assurance, and audit checkpoints
- Experience deploying Generative AI solutions in on-premises, hybrid, and public cloud environments
- Hands-on experience with at least one of Microsoft Azure, AWS, or Google Cloud Platform
- Experience applying data mining and machine learning techniques, including decision trees, neural networks, SVMs, anomaly detection, recommender systems, pattern discovery, and text mining
Honeywell Compensation & Benefits Highlights
The following summarizes recurring compensation and benefits themes identified from responses generated by popular LLMs to common candidate questions about Honeywell and has not been reviewed or approved by Honeywell.
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Retirement Support — Retirement benefits are anchored by a strong 401(k) match with clear vesting and annual funding mechanics. Plan administration and education resources further reinforce long‑term savings support.
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Leave & Time Off Breadth — Time away provisions include company holidays, flexible vacation for many exempt roles, and paid sick time. These policies contribute meaningful breadth beyond base pay.
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Parental & Family Support — Paid parental leave is available to all parents with flexible usage options, and certain family‑building supports are included. Birth mothers can coordinate leave with short‑term disability for extended coverage.
Honeywell Insights
What We Do
Honeywell is a Fortune 500 company that invents and manufactures technologies to address tough challenges linked to global macrotrends such as safety, security, and energy. With approximately 110,000 employees worldwide, including more than 19,000 engineers and scientists, we have an unrelenting focus on quality, delivery, value, and technology in everything we make and do.
