**Role Summary**:
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.
**The Core Challenge**:
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 & Infrastructure**:
- Support Snowflake-based AI workflows including **data ingestion, Cortex Agents, Analyst, and Search**:
- 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.
**CI/CD & Automation**:
- Automate infrastructure provisioning and environment configuration using Infrastructure-as-Code
- Enable safe, repeatable deployments with versioning, rollback, and environment promotion strategies
**Observability & Reliability**:
- 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
**Enterprise Integrations**:
- Ensure reliability, security, and observability of API-based and event-driven integrations
**Collaboration & Enablement**:
- Partner closely with AI engineers, data engineers, and IT teams to remove operational blockers
- Provide documentation, templates, and best practices that enable teams to deploy and operate independently




- Contribute to standards for security, reliability, and governance across the GenAI platform
**Required Qualifications**:
- 3+ years in DevOps, platform engineering, or software infrastructure roles; 1-2+ years specifically with ML/AI infrastructure or MLOps
- Strong experience with **CI/CD pipelines** (GitHub Actions preferred)
- 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
- 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 **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 (Ciudad de México)
🏢 Ingersoll Rand
📍 Ciudad de México