Senior AI Platform Engineer – Agentic AI

Honeywell International Inc.; Atlanta (GA); Hybrid; USD 150,000 - 190,000; Full time

Job summary

Honeywell International Inc. in Atlanta, GA seeks a Senior AI Platform Engineer – Agentic AI to design and build enterprise platform capabilities that enable teams to create, deploy, evaluate, observe, and operate production-grade AI agents at scale.

This hands-on role focuses on reusable services, SDKs, and patterns to accelerate AI development across Honeywell. You will work across software engineering, AI platform engineering, Generative AI, data engineering, and MLOps, collaborating with

Qualifications

• 8+ years of software, data platform, ML platform, or AI engineering experience.
• Strong Python software engineering expertise, including API/service development.
• Experience building cloud-native distributed applications and platforms.
• Hands-on experience developing and deploying AI/ML and Generative AI solutions.
• Experience building LLM and agentic AI applications, including RAG/grounding and tool integration.

Responsibilities

• Design and build reusable enterprise platform capabilities for AI agents and multi-agent workflows.
• Develop orchestration patterns for agentic AI frameworks without coupling to a single framework.
• Build services for RAG and enterprise knowledge retrieval, including document ingestion and vector search.
• Design and operate scalable platform services and APIs using Python and FastAPI or similar.
• Implement end-to-end observability, tracing, monitoring, and alerting across AI workflows.
• Ensure security, reliability, and governance of enterprise AI services.

Skills

Python

API / service development

Distributed systems

CI/CD and observability

AI/ML / Generative AI experience

Education

Bachelor's or advanced degree in Computer Science, Engineering, AI, or related discipline

Tools

LangGraph

LangChain

Semantic Kernel

AutoGen

LangSmith

MLflow

Azure Machine Learning

Azure AI services

Databricks

Kubernetes

Vector databases

Neo4j

Job description

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.

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.

Qualifications

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