Senior Engineer - LLMOps & MLOps
Telecommuter TN, United States
R
USD 140K-215K (estimate) Senior-level Full Time
Tasks
- Automate feature stores and feature engineering
- Build automated CI CD pipelines for AI
- Build continuous training pipelines
- Build data pipelines
- Containerize and orchestrate inference services
- Deploy and scale LLM applications
- Implement RAG infrastructure
- Implement automated model evaluation
- Implement model prompt and weight versioning
- Implement security scanning and guardrails
- Ingest legacy data into cloud workflows
- Manage AI infrastructure with IaC
- Manage IAM roles VPC peering and firewall rules
- Manage vector databases
- Measure model performance with metrics
- Monitor drift hallucinations latency and token usage
- Optimize model serving endpoints
- Secure and harden AI resources
- Serve models with low latency and high throughput
- Set up observability and monitoring
- Transition notebooks to production services
Perks/Benefits
Skills/Tech-stack
AI Search | AI Studio | AWS | AWS Step Functions | Airflow | Amazon Bedrock | Amazon SageMaker | Azure | Azure AI | Azure AI Search | Azure AI Studio | CI/CD | CloudFormation | Continuous Training | Docker | Docker Kubernetes | Docker Swarm | Drift Detection | Feature Engineering | Feature Store | Firewall Configuration | Hallucination detection | IAM | Infrastructure as Code | Kubeflow | Kubernetes | LLM Evaluation | Latency optimization | Machine Learning | Machine Learning Model Validation | Meteor | Model Drift | Model Drift Detection | Model Monitoring | Model Validation | OpenSearch | Pinecone | PromptOps | PySpark | Python | ROUGE | Retrieval-Augmented Generation | SQL | Serverless computing | Statistical validation | Step Functions | Terraform | Token Usage Monitoring | Token usage | Usage monitoring | VPC Peering | Vector Database | Version control | “as-code”
Education
Bachelor of Engineering | Bachelor of Science | Master of Science
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