Senior AI Platform Engineer – Agentic AI

We are seeking a Senior AI Platform Engineer Agentic AI to join our Data Engineering AI & ML Platform team. This role will help design and build the enterprise platform capabilities that enable teams to create deploy evaluate observe and operate production-grade AI agents and intelligent applications at scale.

You will work at the intersection of software engineering AI platform engineering Generative AI agentic systems data engineering and MLOps. A major focus of the role will be building reusable platform capabilities for agent development including reasoning and orchestration workflows tool integration enterprise knowledge retrieval memory/context management evaluation observability and responsible operation of AI agents.

Rather than building one-off AI applications you will help establish reusable services SDKs patterns and platform components that accelerate AI development across Honeywell.

You will collaborate with data engineers AI/ML engineers application teams architects product teams and domain experts to take emerging AI capabilities from experimentation to secure reliable production-grade enterprise services.

This is a hands-on individual contributor role for an engineer who is comfortable solving ambiguous problems developing platform capabilities and turning rapidly evolving AI technologies into maintainable production systems.

The role is based in Atlanta GA on a hybrid basis. Note: For the first 90 days new hires must be prepared to work onsite 100% Monday through Friday.

KEY RESPONSIBILITIES
Agentic AI Platform

• Design and build reusable enterprise platform capabilities for developing deploying and operating AI agents and multi-agent workflows.
• Build frameworks and reusable components that allow agents to securely interact with enterprise APIs data platforms applications tools and knowledge sources.
• Develop orchestration patterns using agentic AI frameworks without coupling the platform to a single framework.
• Build services supporting RAG and enterprise knowledge retrieval including document ingestion retrieval pipelines vector/semantic search grounding citations and contextual enrichment.
• Develop systematic approaches for agent and LLM evaluation including task success response quality grounding tool-use correctness latency reliability and cost.
• Implement observability and tracing for agent execution so developers can understand agent decisions model calls tool calls failures performance and resource consumption.

AI Platform Engineering

• Design build and operate scalable platform services and APIs using Python and FastAPI or comparable production frameworks.
• Build reusable APIs SDKs libraries templates and reference implementations that make it easier for engineering teams to develop and deploy AI applications.
• Design cloud-native AI platform services using technologies such as Azure Kubernetes Databricks Azure
• Machine Learning MLflow or equivalent platforms.
• Develop production capabilities for model and application deployment configuration versioning experimentation evaluation and lifecycle management.
• Build automated CI/CD and testing pipelines for AI services agent workflows prompts/configurations and supporting platform components.
• Convert prototypes and notebook-based experimentation into modular testable maintainable production-quality software.
• Design platform patterns for maintaining data quality lineage access control and traceability across AI workflows.

Production Engineering Safety & Reliability

• Build AI services with production software engineering practices covering testing security scalability reliability and maintainability.
• Implement end-to-end observability tracing monitoring and alerting across agent workflows models APIs retrieval systems and platform services.
• Establish mechanisms for handling agent failures tool failures model errors timeouts retries and degraded dependencies.
• Implement appropriate guardrails access controls auditability and human oversight for enterprise agentic AI workflows.
• Optimize AI workloads for latency throughput infrastructure utilization and model/token cost.
• Diagnose complex production issues spanning AI models orchestration data APIs infrastructure and applications.

YOU MUST HAVE

• 8 years of software data platform ML platform or AI engineering experience including designing building and supporting production-grade systems.
• Strong Python software engineering expertise including API/service development testing debugging packaging design patterns and modern software development best practices.
• Experience building cloud-native distributed applications and platforms using containers Kubernetes public cloud technologies CI/CD pipelines observability and reliability engineering.
• Hands-on experience developing and deploying AI/ML and Generative AI solutions including model serving lifecycle management monitoring operational support and production deployments.
• Experience building LLM and agentic AI applications including RAG/grounding enterprise data integration and retrieval (structured and unstructured data) orchestration frameworks and tool/API integration.
  

WE VALUE

• Experience with LangGraph LangChain Semantic Kernel AutoGen or equivalent agent orchestration frameworks.
• Experience designing multi-agent systems agent tool ecosystems planning/reasoning workflows agent memory or human-in-the-loop patterns.
• Experience with AI evaluation and observability platforms such as LangSmith MLflow or equivalent tooling.
• Experience with Azure Machine Learning Azure AI services Databricks Kubernetes or comparable AI/data platforms.
• Experience with vector databases semantic search knowledge graphs RDF/SPARQL Neo4j ontology engineering or GraphRAG-style knowledge systems.
• Experience building AI systems that integrate with industrial IoT operational technology building systems digital twins or high-volume telemetry.
• Experience building developer platforms SDKs internal frameworks or reusable engineering capabilities used by multiple development teams.
• Bachelors or advanced degree in Computer Science Engineering Artificial Intelligence or a related discipline or equivalent practical experience.

ABOUT HONEYWELL

Honeywell International Inc. (NYSE: HON) invents and commercializes technologies that address some of the worlds most critical challenges around energy safety security air travel productivity and global urbanization. We are a leading software-industrial company committed to introducing state-of-the-art technology solutions to improve efficiency productivity sustainability and safety in high growth businesses in broad-based attractive industrial end markets. Our products and solutions enable a safer more comfortable and more productive world enhancing the quality of life of people around the globe. Learn more here: OF WORKING FOR HONEYWELL

In addition to a performance-driven salary cutting-edge work and developing solutions side-by-side with dedicated experts in their fields Honeywell employees are eligible for a comprehensive benefits package. This package includes employer-subsidized Medical Dental Vision and Life Insurance; Short-Term and Long-Term Disability; 401(k) match Flexible Spending Accounts Health Savings Accounts EAP and Educational Assistance; Parental Leave Paid Time Off (for vacation personal business sick time and parental leave) and 12 Paid Holidays. For more information: application period for the job is estimated to be 40 days from the job posting date; however this may be shortened or extended depending on business needs and the availability of qualified candidates. Posting date: October 26th 2026