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 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.
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 genai applications using gcp and snowflake container services
support snowflake-based ai workflows including data ingestion, cortex agents, analyst, and search
define standardized, reusable infrastructure patterns for ai applications across development, staging, and production environments
implement cost-aware infrastructure patterns (warehouse sizing, service isolation, token budgeting) for genai workloads
explore, 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.
build and maintain ci/cd pipelines using github for ai applications and platform services
automate infrastructure provisioning and environment configuration using infrastructure-as-code
enable safe, repeatable deployments with versioning, rollback, and environment promotion strategies
implement observability for genai systems using langfuse and snowflake observability tools to continuously improve ai system reliability and usefulness.
cloud & container operations
manage containerized workloads across gcp and snowflake containers
ensure secure networking, secrets management, access controls, and environment isolation
optimize performance, scalability, and cost for ai application workloads
support and operationalize integrations between genai applications and enterprise systems such as sap, salesforce, sharepoint, and other internal/external platforms
partner closely with ai engineers, data engineers, and it teams to remove operational blockers
3+ years in devops, 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 (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
~ 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 applications
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
continuous learner who keeps current with rapidly evolving ai‐ops ecosystem and cloud‐native technologies
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.