MLOps Senior Engineer – Vector/LLDS Database & AI Platform Focus(Only W2 Candidate)
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This role is for an MLOps Senior Engineer focusing on Vector/LLDS Database & AI Platform, requiring 10+ years of experience. Contract length is W2, onsite in Charlotte, NC, or Irving, TX. Key skills include Python, Elasticsearch, and big data technologies.
🌎 - Country
United States
💱 - Currency
$ USD
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💰 - Day rate
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🗓️ - Date discovered
August 28, 2025
🕒 - Project duration
Unknown
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🏝️ - Location type
On-site
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📄 - Contract type
W2 Contractor
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🔒 - Security clearance
Unknown
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📍 - Location detailed
Austin, Texas Metropolitan Area
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🧠 - Skills detailed
#Shell Scripting #Automation #Docker #AI (Artificial Intelligence) #Data Exploration #Debugging #Kubernetes #Splunk #Monitoring #Azure #Elasticsearch #Scripting #Data Ingestion #Grafana #Linux #Programming #Big Data #Cloud #GCP (Google Cloud Platform) #Data Science #Observability #Hadoop #Databases #Data Pipeline #Disaster Recovery #Deployment #Indexing #Python
Role description
Position: MLOps Senior Engineer – Vector/LLDS Database & AI Platform Focus
Location : Charlotte NC & Irving ,Texas (Onsite day 1)
Duration: W2 (Contract )Candidate Only
Job Description::
Experience – 10 +
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
Position: MLOps Senior Engineer – Vector/LLDS Database & AI Platform Focus
Location : Charlotte NC & Irving ,Texas (Onsite day 1)
Duration: W2 (Contract )Candidate Only
Job Description::
Experience – 10 +
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
