2 weeks ago

AI DevOps Engineer

✓ Verified Job

Canonical indexed version, validated from employer's careers page.

Source: Company careers pageValidated by: Fursa AI

Last checked: 3 hours agoStatus: Live

Job Summary

Design, build, and operate production infrastructure for GenAI applications across the enterprise, with a focus on DevOps, cloud infrastructure, CI/CD, observability, and platform reliability. Own the operational lifecycle of LLM-powered systems, including prompt versioning, model configuration, cost controls, and reliable deployments on Snowflake-native and API-based GenAI platforms. Partner with AI and IT teams to deliver secure, observable, scalable AI that integrates smoothly with enterprise systems.

Location: Mexico

Workplace: Onsite

Employment Type: Full time

Job Function: DevOps, Cloud & Infrastructure

Seniority: Mid level

Key Responsibilities

•Design, build, and maintain cloud infrastructure to host GenAI applications using GCP and Snowflake container services.
•Support Snowflake-based AI workflows including data ingestion, Cortex Agents, Analyst, and Search.
•Create standardized infrastructure patterns and cost-aware configurations for GenAI workloads across environments.
•Build CI/CD pipelines with GitHub and automate environment provisioning using Infrastructure-as-Code.
•Implement observability and operational practices to monitor health, latency, usage, errors, and cost; support enterprise system integrations.

Key Requirements

•3+ years in DevOps, platform engineering, or software infrastructure, with 1–2+ years in ML/AI infrastructure or MLOps.
•Operate LLM-based applications in production, including prompt management, cost monitoring, and reliability practices.
•Build and maintain CI/CD pipelines using GitHub actions and automate provisioning with Infrastructure-as-Code.
•Hands-on container experience using Docker and Kubernetes (or managed container platforms).
•Work on GCP workloads and implement observability, monitoring, and API-based integrations for production systems.

Experience:3+ yearsDevOpsMLOpsGenAILLMCloud infrastructure

Education:Bachelor's in Computer Science, Software Engineering, IT, or related field

Skills:CommunicationCollaborationTroubleshooting

Languages:English

Tech Stack:GCPSnowflakeLLM APIsCortex AgentsAnalystSearchGitHubGitHub ActionsInfrastructure-as-CodeTerraformDockerKubernetesPythonBashLangfuseSAPSalesforceSharePoint

Company Brief

Ingersoll Rand

Designs, manufactures, and services industrial equipment and technologies including air compressors, power tools, fluid management systems, and downstream industrial solutions for manufacturing, construction, and commercial customers worldwide.

Industry: Industrial Machinery

Company Size: Enterprise (1,001+ employees)

Revenue: USD 1B+

Growth: Public Company

Valuation: Public Company (Market Cap in USD)

Funding: IPO / Publicly Listed

Headquarters: Davidson, United States

Founded: 1871

Website LinkedIn

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Ingersoll Rand

2 weeks ago

AI DevOps Engineer

✓ Verified Job

Canonical indexed version, validated from employer's careers page.

Source: Company careers pageValidated by: Fursa AI

Last checked: 3 hours agoStatus: Live

Job Summary

Location: Mexico

Workplace: Onsite

Employment Type: Full time

Job Function: DevOps, Cloud & Infrastructure

Seniority: Mid level

Key Responsibilities

•Design, build, and maintain cloud infrastructure to host GenAI applications using GCP and Snowflake container services.
•Support Snowflake-based AI workflows including data ingestion, Cortex Agents, Analyst, and Search.
•Create standardized infrastructure patterns and cost-aware configurations for GenAI workloads across environments.
•Build CI/CD pipelines with GitHub and automate environment provisioning using Infrastructure-as-Code.
•Implement observability and operational practices to monitor health, latency, usage, errors, and cost; support enterprise system integrations.

Key Requirements

•3+ years in DevOps, platform engineering, or software infrastructure, with 1–2+ years in ML/AI infrastructure or MLOps.
•Operate LLM-based applications in production, including prompt management, cost monitoring, and reliability practices.
•Build and maintain CI/CD pipelines using GitHub actions and automate provisioning with Infrastructure-as-Code.
•Hands-on container experience using Docker and Kubernetes (or managed container platforms).
•Work on GCP workloads and implement observability, monitoring, and API-based integrations for production systems.

Experience:3+ yearsDevOpsMLOpsGenAILLMCloud infrastructure

Education:Bachelor's in Computer Science, Software Engineering, IT, or related field

Skills:CommunicationCollaborationTroubleshooting

Languages:English

Tech Stack:GCPSnowflakeLLM APIsCortex AgentsAnalystSearchGitHubGitHub ActionsInfrastructure-as-CodeTerraformDockerKubernetesPythonBashLangfuseSAPSalesforceSharePoint

Company Brief

Ingersoll Rand

Industry: Industrial Machinery

Company Size: Enterprise (1,001+ employees)

Revenue: USD 1B+

Growth: Public Company

Valuation: Public Company (Market Cap in USD)

Funding: IPO / Publicly Listed

Headquarters: Davidson, United States

Founded: 1871

Website LinkedIn