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.Enable and scale Ingersoll Rand's Gen AI program by designing, building, and operating the production infrastructure that powers AI-driven applications across the enterprise. This role focuses on Dev Ops, cloud infrastructure, CI/CD, observability, and platform reliability for Gen AI systems built on LLM 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 Gen AI platforms.You will work closely with AI engineers and application developers to turn prototypes into secure, reliable, observable, and scalable AI applications , ensuring smooth integration with enterprise systems and data platforms. This is a Dev Ops and platform engineering role with a strong focus on production-grade AI systems.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 providing standardized infrastructure, deployment pipelines, and operational frameworks so AI teams can move fast without sacrificing reliability, security, or governance.Design, build,



and maintain cloud infrastructure to host Gen AI applications using GCP and Snowflake container servicesSupport Snowflake-based AI workflows including data 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 Gen AI workloadsExplore, build, and support proof‐of‐concept initiatives to evaluate emerging Gen AI and MLOps platforms and architectures, focusing on deployment, orchestration, monitoring, and governance of LLM-based systems.Build and maintain CI/CD pipelines using Git Hub for AI applications and platform servicesAutomate infrastructure provisioning and environment configuration using Infrastructure-as-CodeEnable safe, repeatable deployments with versioning, rollback, and environment promotion strategiesImplement observability for Gen AI systems using Langfuse and Snowflake observability tools to continuously improve AI system reliability and usefulness.Cloud & Container OperationsManage containerized workloads across GCP and Snowflake containersEnsure secure networking, secrets management, access controls, and environment isolationOptimize performance, scalability, and cost for AI application workloadsSupport and operationalize integrations between Gen AI applications and enterprise systems such as SAP, Salesforce, Share Point,



and other internal/external platformsPartner closely with AI engineers, data engineers, and IT teams to remove operational blockers3+ years in Dev Ops, platform engineering, or software infrastructure roles; Experience operating LLM‐based applications in production, including prompt management, cost monitoring, and reliability practices~ Strong experience with CI/CD pipelines (Git Hub Actions preferred)~ Hands‐on experience with containerized applications (Docker; Kubernetes or managed container platforms)~ Experience operating workloads on GCP or similar cloud platforms~ Proficiency with Infrastructure‐as‐Code tools (Terraform or equivalent)~ Strong scripting skills (Python and/or Bash)~ Experience implementing monitoring, logging, and observability for production systems~ Fluent in English (written and spoken)~ Bachelor's or Master's degree in Computer Science, Software Engineering, IT, or related field (or equivalent experience) Experience with Snowflake , including data ingestion pipelines and Snowflake‐native applicationsExperience with data versioning tools (DVC, Pachyderm, Lake FS)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 Io T experienceContinuous learner who keeps current with rapidly evolving AI‐Ops ecosystem and cloud‐native technologiesCustomers 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.

📌 Ai devops engineer (Ciudad de México)
🏢 Ingersoll-Rand
📍 Ciudad de México