Overview
On Site
Depends on Experience
Accepts corp to corp applications
Contract - W2
Contract - Independent
Contract - 12 Month(s)
Able to Provide Sponsorship
Skills
AIML Support
Artificial Intelligence
Apache Hadoop
Backup & Restore
Big Data
Cloud Computing
Continuous Integration
Debugging
Disaster Recovery
Docker
Elasticsearch
Generative Artificial Intelligence (AI)
Google Cloud Platform
Grafana
Kubernetes
Linux
Python
Modeling
Microsoft Azure
Machine Learning Operations (ML Ops)
Splunk
Shell Scripting
Data Science
Database
Job Details
Hello
Please check the below position and reply back with the details and updated resume if you are interested.
Job title: MLOps Senior Engineer Vector/LLDS Database & AI Platform Focus
Location: North Carolina
Duration: Longterm
Expeience 10+ Years
This role is not for an AIML developer. We re specifically 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 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.
Thanks,
Max | KLNtek
Lead - Recruitment
Email:
US:
India: +91-
324 E Foothill Blvd, Ste 206, 91006 Arcadia, California
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