Job description

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. • Bachelor's 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 world’s 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: honeywell.com BENEFITS 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: benefits.honeywell.com The 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 Design, build, and operate enterprise-scale AI and agentic AI platforms that enable the development, deployment, orchestration, and governance of AI applications and multi-agent systems. Develop reusable platform services, APIs, frameworks, and cloud-native solutions that support LLMs, RAG, enterprise data integration, and knowledge retrieval while ensuring scalability, reliability, security, observability, and cost efficiency. Drive production engineering best practices through CI/CD, testing, monitoring, lifecycle management, and operational support, while implementing guardrails, governance, and performance optimization for mission-critical AI workloads.

Requirements

Must-haves

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

Nice-to-haves

None flagged.

Application prep

Use the role’s language and application details while tailoring your materials.

Keywords to mirror

aimlllmpythonazurekubernetesmachine learningci/cdapi

Platform: oracle-cloud
Effort: medium

Benefits

Benefits detected in the listing.

Dental

Vision

401(K)

Paid Time Off

Parental Leave