MLOps Senior Engineer – Vector/LLDS Database & AI Platform Focus -- Charlotte NC & Irving, TX

Hi , Hope you are doing great! We have the below urgent position with my client. Please reply if you are interested. Job Title : MLOps Senior Engineer – Vector/LLDS Database & AI Platform Focus Location : Charlotte NC & Irving, TX Long Term Contract “This role is not for an AIML developer. We’re specifically looking for someone with the expertise outlined in the attachment—someone who can support the platforms our developers use, rather than build AI/GenAI solutions themselves. It’s essential that candidates are screened carefully against these requirements before resumes are submitted. While many engineers enjoy developing AI solutions, fewer are equipped or inclined to support the environments that enable them” 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.