Lead, AI Engineering
Charlotte, North Carolina, United States
USD 180K-220K Senior-level Full Time
Tasks
- Build AI dashboards and observability platforms to monitor model performance
- Build and govern MLOps and LLMOps frameworks with CI CD model registries and retraining
- Collaborate cross functionally to deliver production grade AI solutions
- Design ETL ELT pipelines in Python for training inference and RAG
- Design and oversee resilient AI infrastructure components for training and inference
- Design end-to-end AI & data architectures
- Establish AI governance standards for data quality security privacy and compliance
- Implement monitoring and alerting for latency hallucination risk drift and health
- Lead design and implementation of scalable AI platforms
- Lead machine learning and generative AI model training fine tuning deployment and lifecycle management
- Mentor and lead AI engineers and platform teams
- Partner with cybersecurity and compliance teams
Perks/Benefits
Skills/Tech-stack
AWS Firehose | AWS Glue | AWS SageMaker | Amazon Kinesis | Automated retraining | Azure Machine Learning | CD pipelines | CI/CD | CI/CD pipelines | Cassandra | Databricks | Delta Lake | Delta Lakes | Distributed Computing | Docker | DocumentDB | DynamoDB | ELT | ETL | Embeddings | GPU-enabled Compute | Generative AI | Google Vertex | Google Vertex AI | Inference monitoring | InfluxDB | Infrastructure as Code | Kubernetes | LLMOps | Lakehouse Architecture | Langchain | Language Models | Large Language Models | MLOps | Machine Learning | Model Registry | MongoDB | Observability | Prompt versioning | PyTorch | Python | RAG | RDS | SQL | Scikit-learn | TensorFlow | Terraform | Vector Databases | Vertex AI | “as-code”
Education
Roles
AI | AI Engineer | AI Engineering | AI Engineering Manager | Engineer | Engineering Manager | Lead | Lead AI Engineer | Manager
Regions
Countries
States
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