Software Engr II
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Key skills for this role
About the Role
As a Software Engr II here at Honeywell, you will design, develop, and maintain software applications, collaborate with cross-functional teams, and ensure timely project completion. Impact innovative solutions driving efficiency and productivity.
Key Skills for This Role
Full Job Posting
Responsibilities
- 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.
- 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, testing, and CI/CD pipelines.
- 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.
- Retrieval-Augmented Generation (RAG)
- Experience designing and implementing advanced RAG architectures.
- Hands-on experience with:
- Vector databases
- Embeddings
- Semantic search
- Enterprise knowledge retrieval systems
- 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 with cloud platforms such as:
- Microsoft Azure
- AWS
- Google Cloud Platform (GCP)
- Infrastructure & Platforms
- Experience with:
- Docker
- Kubernetes
- Containerized deployments
- Understanding of scalable AI infrastructure and distributed systems.
- Data Science & Machine Learning
- 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.
- Integration & Enterprise Systems
- Experience integrating AI solutions with:
- Enterprise APIs
- Databases
- Internal and external data sources
- Business applications and workflows
- - 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. 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, testing, and CI/CD pipelines. 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. Retrieval-Augmented Generation (RAG) - Experience designing and implementing advanced RAG architectures. - Hands-on experience with: - Vector databases - Embeddings - Semantic search - Enterprise knowledge retrieval systems
Qualifications
- Preferred Qualifications
- 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.
- Education
- Bachelor's or master's degree in computer science, Artificial Intelligence, Machine Learning, Data Science, or a related technical discipline.
- Preferred Qualifications - 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. Education Bachelor's or master's degree in computer science, Artificial Intelligence, Machine Learning, Data Science, or a related technical discipline.
About Honeywell
Honeywell is a global industrial technology company that provides products, software and solutions for building, process and industrial automation. Its technologies help customers improve safety, productivity, energy efficiency and operational reliability.
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