InfiCare Technologies
MLOps Senior Engineer Vector/LLDS Database & AI Platform Focus
InfiCare Technologies, Irving, Texas, United States, 75084
Job Title:
MLOps Senior Engineer Vector/LLDS Database & AI Platform Focus Location:
Charlotte NC & Irving, Texas (Onsite day) Mode Of Hire:
Contract Mode Of Work:
Onsite 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 (X week every X X 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 Google Cloud Platform 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 Focus Location:
Charlotte NC & Irving, Texas (Onsite day) Mode Of Hire:
Contract Mode Of Work:
Onsite 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 (X week every X X 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 Google Cloud Platform 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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