ZipRecruiter
MLOps Senior Engineer Vector/ LLDS Database & AI Platform
ZipRecruiter, Charlotte, North Carolina, United States, 28245
Job DescriptionJob Description
Job Title:
MLOps Senior Engineer – Vector/LLDS Database AI Platform Location –
Charlotte NC & Irving ,Texas (Onsite day 1) Focus Core Technical Skills - Vector Databases: Hands-on experience with Elasticsearch or similar; understanding of similarity search, indexing strategies, and embedding management. - Linux Systems: Strong command-line skills; shell scripting; system-level monitoring and debugging. - Python Programming: Proficient in automation scripting; experience in building AI models, data pipelines, and OpenAI integrations. - Big Data Technologies: Familiarity with Hadoop-based platforms like MapR and Hortonworks. AI Platform Production Support - Experience supporting predictive AI workloads in production. - Troubleshooting across data ingestion, model inference, and deployment layers. - Familiarity with CI/CD pipelines and containerization (Docker, Kubernetes). - On-call support for GenAI and predictive pipelines (1 week every 6–8 weeks). - Understanding of enterprise disaster recovery (DR) solutions including backup and restore. Observability Monitoring - Ability to define and implement observability strategies for AI systems. - Experience with tools such as Splunk, Grafana, ELK stack, OpenTelemetry. - Proactive monitoring of model failures, latency, and system health. Bonus Qualifications - Multi-cloud Experience: Exposure to GCP and Azure environments. - Data Science Lifecycle: Involvement in full-cycle projects including problem definition, data exploration, modeling, evaluation, training, scoring, and
operationalization. - MLOps Principles: Understanding of model lifecycle management and collaboration with data scientists to deploy solutions.
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MLOps Senior Engineer – Vector/LLDS Database AI Platform Location –
Charlotte NC & Irving ,Texas (Onsite day 1) Focus Core Technical Skills - Vector Databases: Hands-on experience with Elasticsearch or similar; understanding of similarity search, indexing strategies, and embedding management. - Linux Systems: Strong command-line skills; shell scripting; system-level monitoring and debugging. - Python Programming: Proficient in automation scripting; experience in building AI models, data pipelines, and OpenAI integrations. - Big Data Technologies: Familiarity with Hadoop-based platforms like MapR and Hortonworks. AI Platform Production Support - Experience supporting predictive AI workloads in production. - Troubleshooting across data ingestion, model inference, and deployment layers. - Familiarity with CI/CD pipelines and containerization (Docker, Kubernetes). - On-call support for GenAI and predictive pipelines (1 week every 6–8 weeks). - Understanding of enterprise disaster recovery (DR) solutions including backup and restore. Observability Monitoring - Ability to define and implement observability strategies for AI systems. - Experience with tools such as Splunk, Grafana, ELK stack, OpenTelemetry. - Proactive monitoring of model failures, latency, and system health. Bonus Qualifications - Multi-cloud Experience: Exposure to GCP and Azure environments. - Data Science Lifecycle: Involvement in full-cycle projects including problem definition, data exploration, modeling, evaluation, training, scoring, and
operationalization. - MLOps Principles: Understanding of model lifecycle management and collaboration with data scientists to deploy solutions.
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