Lead, AI Engineering
Charlotte, North Carolina, United States
USD 180K-220K Senior-level Full Time
Tasks
- Build AI powered dashboards
- Build RAG workflows
- Build feature stores
- Build model ready data pipelines
- Build vector databases
- Deploy production AI solutions
- Design AI/data architectures
- Design ETL ELT pipelines in Python
- Enable automated retraining
- Ensure high availability scalability reliability
- Establish AI governance standards
- Implement AI infrastructure components
- Implement LLMOps frameworks
- Implement MLOps frameworks
- Implement inference monitoring
- Implement model observability
- Implement monitoring and alerting
- Implement prompt versioning
- Lead AI platform design
- Manage ML model lifecycle
- Mentor AI engineers
- Monitor model performance and drift
- Partner with cybersecurity and compliance teams
- Prototype emerging AI capabilities
- Set up CI/CD pipelines for models
- Train fine-tune deploy ML models
Perks/Benefits
Skills/Tech-stack
AWS Glue | AWS RDS | AWS SageMaker | Amazon Kinesis | Amazon Kinesis Firehose | Automated retraining | Azure Machine Learning | CI/CD | Cassandra | Databricks | Delta Lake | Docker | DocumentDB | Drift Detection | DynamoDB | ELT | ETL | Embeddings | Feature Store | GPU Computing | Generative AI | InfluxDB | Infrastructure as Code | Kinesis Firehose | Kubernetes | LLMOps | Lakehouse | Langchain | Language Models | Large Language Models | MLOps | Machine Learning | Model Registry | MongoDB | Observability | Prompt versioning | PyTorch | Python | RAG | Retrieval-Augmented Generation | SQL | Scikit-learn | TensorFlow | Terraform | Vector Database | Vertex AI | “as-code”
Education
Regions
Countries
States
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