{"id":3471385,"url":"https://alion.io/job/honeywell-senior-ai-platform-engineer-agentic-ai","title":"Senior AI Platform Engineer – Agentic AI","company":{"id":15856,"name":"Honeywell","domain":"honeywell.com","url":"https://alion.io/company/honeywell-com","size_band":"5000+","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Oracle","truth_index":{"grade":"B","score":79,"open_postings":87,"ghost_share":0,"stale_share":0.828,"repost_share":0,"time_to_fill_p50_days":27,"computed_at":"2026-10-31T05:58:55Z"}},"role":"AI/ML","role_family":"AI/ML","seniority":"senior","employment_type":null,"work_mode":"hybrid","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Atlanta, United States"],"countries":["US"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":127000,"max_usd":244000,"period":"year","method":"role_seniority_country_remote_cell","sample_n":1332},"experience_years_min":8,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"AI Agents","optional":false},{"name":"AutoGen","optional":false},{"name":"Azure","optional":false},{"name":"CI/CD","optional":false},{"name":"Databricks","optional":false},{"name":"Digital Twin","optional":false},{"name":"FastAPI","optional":false},{"name":"GraphRAG","optional":false},{"name":"Human-in-the-Loop","optional":false},{"name":"Knowledge Graph","optional":false},{"name":"Kubernetes","optional":false},{"name":"LangChain","optional":false},{"name":"LangGraph","optional":false},{"name":"LangSmith","optional":false},{"name":"LLM","optional":false},{"name":"Machine Learning","optional":false},{"name":"MLFlow","optional":false},{"name":"Multi-Agent Systems","optional":false},{"name":"Neo4j","optional":false},{"name":"Platform Engineering","optional":false},{"name":"Python","optional":false},{"name":"RAG","optional":false},{"name":"Semantic Kernel","optional":false},{"name":"Semantic Search","optional":false}],"status":"live","first_seen_at":"2026-10-26T18:11:57Z","employer_posted_date":"2026-10-26","last_verified_at":"2026-10-31T15:59:32Z","board_verified":true,"closed_at":null,"days_open":5,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":5},"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.\nYou 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.\nRather than building one-off AI applications, you will help establish reusable services, SDKs, patterns, and platform components that accelerate AI development across Honeywell.\nYou 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.\nThis 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.\nThe 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.\nKEY RESPONSIBILITIES\nAgentic AI Platform\nDesign and build reusable enterprise platform capabilities for developing, deploying, and operating AI agents and multi-agent workflows.\nBuild frameworks and reusable components that allow agents to securely interact with enterprise APIs, data platforms, applications, tools, and knowledge sources.\nDevelop orchestration patterns using agentic AI frameworks, without coupling the platform to a single framework.\nBuild services supporting RAG and enterprise knowledge retrieval, including document ingestion, retrieval pipelines, vector/semantic search, grounding, citations, and contextual enrichment.\nDevelop systematic approaches for agent and LLM evaluation, including task success, response quality, grounding, tool-use correctness, latency, reliability, and cost.\nImplement observability and tracing for agent execution so developers can understand agent decisions, model calls, tool calls, failures, performance, and resource consumption.\nAI Platform Engineering\nDesign, build, and operate scalable platform services and APIs using Python and FastAPI or comparable production frameworks.\nBuild reusable APIs, SDKs, libraries, templates, and reference implementations that make it easier for engineering teams to develop and deploy AI applications.\nDesign cloud-native AI platform services using technologies such as Azure, Kubernetes, Databricks, Azure \nMachine Learning, MLflow, or equivalent platforms.\nDevelop production capabilities for model and application deployment, configuration, versioning, experimentation, evaluation, and lifecycle management.\nBuild automated CI/CD and testing pipelines for AI services, agent workflows, prompts/configurations, and supporting platform components.\nConvert prototypes and notebook-based experimentation into modular, testable, maintainable, production-quality software.\nDesign platform patterns for maintaining data quality, lineage, access control, and traceability across AI workflows.\nProduction Engineering, Safety & Reliability\nBuild AI services with production software engineering practices covering testing, security, scalability, reliability, and maintainability.\nImplement end-to-end observability, tracing, monitoring, and alerting across agent workflows, models, APIs, retrieval systems, and platform services.\nEstablish mechanisms for handling agent failures, tool failures, model errors, timeouts, retries, and degraded dependencies.\nImplement appropriate guardrails, access controls, auditability, and human oversight for enterprise agentic AI workflows.\nOptimize AI workloads for latency, throughput, infrastructure utilization, and model/token cost.\nDiagnose complex production issues spanning AI models, orchestration, data, APIs, infrastructure, and applications.\n YOU MUST HAVE\n8+ years of software, data platform, ML platform, or AI engineering experience, including designing, building, and supporting production-grade systems.\nStrong Python software engineering expertise, including API/service development, testing, debugging, packaging, design patterns, and modern software development best practices.\nExperience building cloud-native distributed applications and platforms using containers, Kubernetes, public cloud technologies, CI/CD pipelines, observability, and reliability engineering.\nHands-on experience developing and deploying AI/ML and Generative AI solutions, including model serving, lifecycle management, monitoring, operational support, and production deployments.\nExperience building LLM and agentic AI applications, including RAG/grounding, enterprise data integration and retrieval (structured and unstructured data), orchestration frameworks, and tool/API integration.\nWE VALUE\nExperience with LangGraph, LangChain, Semantic Kernel, AutoGen, or equivalent agent orchestration frameworks.\nExperience designing multi-agent systems, agent tool ecosystems, planning/reasoning workflows, agent memory, or human-in-the-loop patterns.\nExperience with AI evaluation and observability platforms such as LangSmith, MLflow or equivalent tooling.\nExperience with Azure Machine Learning, Azure AI services, Databricks, Kubernetes, or comparable AI/data platforms.\nExperience with vector databases, semantic search, knowledge graphs, RDF/SPARQL, Neo4j, ontology engineering, or GraphRAG-style knowledge systems.\nExperience building AI systems that integrate with industrial IoT, operational technology, building systems, digital twins, or high-volume telemetry.\nExperience building developer platforms, SDKs, internal frameworks, or reusable engineering capabilities used by multiple development teams.\nBachelor's or advanced degree in Computer Science, Engineering, Artificial Intelligence, or a related discipline, or equivalent practical experience.\nABOUT HONEYWELL\nHoneywell 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: https://www.honeywell.com/us/en\nBENEFITS OF WORKING FOR HONEYWELL\nIn 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: https://benefits.honeywell.com/\nThe 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. 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