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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 Summary Enable 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 on DevOps, cloud infrastructure, CI/CD, observability, and platform reliability for GenAI 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 GenAI 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 DevOps and platform engineering role with a strong focus on production-grade AI systems.
The Core Challenge GenAI 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 providing standardized infrastructure, deployment pipelines, and operational frameworks so AI teams can move fast without sacrificing reliability, security, or governance.




Key Responsibilities GenAI Platform 1-2+ years specifically with ML/AI infrastructure or MLOps Experience operating LLM-based applications in production, including prompt management, cost monitoring, and reliability practices Strong experience with CI/CD pipelines (GitHub 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 Experience supporting API-based applications and integrations Ability to troubleshoot and operate complex distributed systems Strong communication skills and ability to collaborate across technical and business teams Fluent in English (written and spoken) Bachelor's or Master's degree in Computer Science, Software Engineering, IT, or related field (or equivalent experience) Preferred Qualifications Experience with Snowflake , including data ingestion pipelines and Snowflake-native applications Familiarity with GenAI application architectures (RAG, agents, prompt orchestration, API-based LLM usage) Experience with Langfuse or similar AI observability tools Experience integrating enterprise systems (SAP, Salesforce, SharePoint, etc.) Experience 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 systems What This Role IS Infrastructure engineer who enables AI teams to move faster through automation and robust tooling Systems thinker who balances reliability, scalability, and cost efficiency Bridge between AI innovation and production operations who translates complex requirements into practical solutions Continuous learner who keeps current with rapidly evolving AI-Ops ecosystem and cloud-native technologies

📌 Ai Devops Engineer (Tlalnepantla de Baz)
🏢 Ingersoll Rand
📍 Tlalnepantla de Baz