Need - AIML Senior Platform Support Engineer in Charlotte, NC&Irving,, TX (Day 1 Onstite)

[Mohammad Sazid]

Hello 

Please check the below position and reply back with the details and updated resume if you are interested.

 

Job title: AIML Senior Platform Support Engineer /MLOps Senior Engineer – Vector/LLDS Database & AI Platform Focus

Location: Irving, TX & Charlotte, NC (Day-1 Onsite)

Duration: Longterm

Experience: 10 to 15 Years

 

Visa – All Visa Except GC (No GC Please)

Passport Number is mandatory

 

This role is not for an AIML developer. looking for—someone who can support the platforms our developers use, rather than build AI/GenAI solutions themselves.

 

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.

Thanks,

Max | KLNtek

Lead - Recruitment

Email: m...@klntek.com

US: +1(315) 274-2836

India: +91-9502141494

324 E Foothill Blvd, Ste 206, 91006 Arcadia, California