Select how often (in days) to receive an alert:Ingersoll Rand is committed to achieving workforce diversity reflective of our communities.
We are an equal opportunity employer.
All qualified applicants will receive consideration for employment without regard to age, ancestry, color, family or medical care leave, gender identity or expression, genetic information, marital status, medical condition, national origin, physical or mental disability, political affiliation, protected veteran status, race, religion, sex (including pregnancy), sexual orientation, or any other characteristic protected by applicable laws, regulations and ordinances.Role SummaryEnable and scale Ingersoll Rand's GenAI program by designing, building, and operating the production infrastructure that powers AI-driven applications across the enterprise.
This role focuses onDevOps, cloud infrastructure, CI/CD, observability, and platform reliabilityfor GenAI systems built onLLM APIs and Snowflake-native capabilities.Own the operational lifecycle of LLM-powered systems including prompt versioning, model configuration, cost controls, and production reliability across Snowflake-native and API-based GenAI platforms.You will work closely with AI engineers and application developers to turn prototypes intosecure, reliable, observable, and scalable AI applications, ensuring smooth integration with enterprise systems and data platforms.
This is a DevOps and platform engineering role with a strong focus on production-grade AI systems.The Core ChallengeGenAI teams can build powerful applications quickly using LLM APIs—but productionizing them at enterprise scale is hard.
Challenges include environment consistency, secure data access, observability, cost control, CI/CD automation, and reliable integrations with core business systems.This role bridges that gap by providingstandardized infrastructure, deployment pipelines, and operational frameworksso AI teams can move fast without sacrificing reliability, security, or governance.Key ResponsibilitiesGenAI Platform & InfrastructureDesign, build, and maintain cloud infrastructure to host GenAI applications usingGCP and Snowflake container servicesSupport Snowflake-based AI workflows includingdata ingestion, Cortex Agents, Analyst, and SearchDefine standardized, reusable infrastructure patterns for AI applications across development, staging, and production environmentsImplement cost-aware infrastructure patterns (warehouse sizing, service isolation, token budgeting) for GenAI workloadsExplore, build, and support proof-of-concept initiatives to evaluate emerging GenAI and MLOps platforms and architectures, focusing on deployment, orchestration, monitoring,



and governance of LLM-based systems.CI/CD & AutomationBuild and maintainCI/CD pipelines using GitHubfor AI applications and platform servicesAutomate infrastructure provisioning and environment configuration using Infrastructure-as-CodeEnable safe, repeatable deployments with versioning, rollback, and environment promotion strategiesObservability & ReliabilityImplement observability for GenAI systems usingLangfuse and Snowflake observability tools to continuously improve AI system reliability and usefulness.Monitor application health, latency, usage, errors, and cost using dashboards, alerts, and runbooks to support reliable production operations.Cloud & Container OperationsManage containerized workloads acrossGCP and Snowflake containersEnsure secure networking, secrets management, access controls, and environment isolationOptimize performance, scalability, and cost for AI application workloadsEnterprise IntegrationsSupport and operationalize integrations between GenAI applications and enterprise systems such asSAP, Salesforce, SharePoint, and other internal/external platformsEnsure reliability, security, and observability of API-based and event-driven integrationsPartner closely with AI engineers, data engineers, and IT teams to remove operational blockersProvide documentation, templates, and best practices that enable teams to deploy and operate independentlyContribute to standards for security, reliability, and governance across the GenAI platformRequired Qualifications3+ years in DevOps, platform engineering, or software infrastructure roles; 1-2+ years specifically with ML/AI infrastructure or MLOpsExperience operating LLM-based applications in production, including prompt management, cost monitoring, and reliability practicesStrong experience withCI/CD pipelines(GitHub Actions preferred)Hands-on experience withcontainerized applications(Docker; Kubernetes or managed container platforms)Experience operating workloads onGCPor similar cloud platformsProficiency withInfrastructure-as-Codetools (Terraform or equivalent)Strong scripting skills (Python and/or Bash)Experience implementingmonitoring, logging, and observabilityfor production systemsExperience supportingAPI-based applications and integrationsAbility to troubleshoot and operate complex distributed systemsStrong communication skills and ability to collaborate across technical and business teamsFluent in English (written and spoken)Bachelor's or Master's degree in Computer Science, Software Engineering, IT,



or related field (or equivalent experience)Preferred QualificationsExperience withSnowflake, including data ingestion pipelines and Snowflake-native applicationsFamiliarity withGenAI application architectures(RAG, agents, prompt orchestration, API-based LLM usage)Experience withLangfuse or similar AI observability toolsExperience with data versioning tools (DVC, Pachyderm, LakeFS)>Knowledge of vector databases and LLM infrastructure (Pinecone, Weaviate, Milvus, Chroma)>Cloud or MLOps certifications (AWS Machine Learning Specialty, AWS Solutions Architect, Kubernetes CKA/CKAD, Azure AI Engineer, GCP ML Engineer)>Manufacturing or industrial IoT experience>Experience with compliance and governance frameworks for AI/ML systemsWhat This Role ISInfrastructure engineer who enables AI teams to move faster through automation and robust toolingSystems thinker who balances reliability, scalability, and cost efficiencyBridge between AI innovation and production operations who translates complex requirements into practical solutionsContinuous learner who keeps current with rapidly evolving AI-Ops ecosystem and cloud-native technologiesIngersoll Rand Inc. (NYSE:IR), driven by an entrepreneurial spirit and ownership mindset, is dedicated to helping make life better for our employees, customers and communities.
Customers lean on us for our technology-driven excellence in mission-critical flow creation and industrial solutions across 40+ respected brands where our products and services excel in the most complex and harsh conditions.
Our employees develop customers for life through their daily commitment to expertise, productivity and efficiency.
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Lean on us to help you make life betterWe think and act like owners.We are committed to making our customers successful.We are bold in our aspirations while moving forward with humility and integrity.We foster inspired teams.Ingersoll Rand is committed to achieving workforce diversity reflective of our communities.
We are an equal opportunity employer.
All qualified applicants will receive consideration for employment without regard to age, ancestry, color, family or medical care leave, gender identity or expression, genetic information, marital status, medical condition, national origin, physical or mental disability, political affiliation, protected veteran status, race, religion, sex (including pregnancy), sexual orientation, or any other characteristic protected by applicable laws, regulations and ordinances.
If you need assistance and/or a reasonable accommodation due to a disability during the application or the recruiting process, please send a request and a member of our team will contact you.
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📌 Ai Devops Engineer (Xico)
🏢 Ingersoll-Rand
📍 Xico