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Senior AI Platform Engineer

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ID de l'offre 308734 Date de publication 07/03/2025 Location : Bangalore, Inde Category  DIGITAL TECHNOLOGY AND DATA (DT&D) Job Status  Salaried Full Time

Job Purpose and Impact

The Senior AI Platform Engineer for AI Ops in AI & Data Science designs, builds and operates the shared MLOps / LLMOps platform that powers Cargill’s data-science and GenAI products. You will own CI/CD pipelines for data ingestion, model training, evaluation and deployment; automate GPU/CPU orchestration across clouds; and embed Responsible-AI, observability and cost-optimization into every stage of the lifecycle. Success is measured by model-to-production velocity, platform uptime, and total-cost-of-ownership improvements 

Key Accountabilities

  • Pipeline & Automation 
    • Implement and maintain reproducible pipelines for data ingestion, feature engineering, model training and deployment using OSS and Commercial toolchains;  
    • Create Terraform modules and GitHub Actions to enable one-click environment provisioning. 
  • GenAI / LLMOps / AgentOps Enablement 
    • Extend platform to support retrieval-augmented generation (RAG) workflows, AI agent workflows, vector databases (pinecone etc), prompt evaluation harnesses, and guardrail policies 
    • Develop automation scripts for GenAIOps.        
  • Observability & SRE: 
    • Instrument Service Level Indicator/Objective (SLIs/SLOs), build dashboards, and lead on-call runbooks;  
    • Monitor system performance and troubleshoot production issues to achieve low latency and availability. 
  • Security & Compliance 
    • Embed IAM, secrets-management, lineage tracking and Responsible-AI checks into pipelines 
    • Produce SOC-2 / ISO-27001-ready documentation.      
  • Coaching & Continuous Improvement 
    • Review pull-requests, run blameless post-incident reviews, and mentor data-science teams on scalable MLOps patterns. 

Qualifications

  • Minimum: 4 years hands-on building ML or data platforms. 

  • Typical: 5–8 years total, including 2 + years operating production MLOps/LLMOps or GPU-accelerated workloads in AWS.

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