Lead AI Platform Engineer
Tasks
- Build internal services and CLI tools for AI developers
- Create workflows and guidance for no code low code agent platforms
- Define tooling and policies for safe local agent usage
- Deploy services to Kubernetes
- Design CI CD and training pipelines
- Design scalable cloud infrastructure using infrastructure as code
- Develop reusable model serving patterns
- Ensure training data quality using feature store
- Implement LLM and agent tracing
- Implement model drift monitoring
- Manage GPU and TPU resource allocation
- Manage vector databases and embedding pipelines
- Mitigate cold start scaling bottlenecks
- Optimize GPU utilization and cloud spend
- Optimize inference for lower latency and higher throughput
- Support AI agent deployment with service templates and tooling
Perks/Benefits
Skills/Tech-stack
AI Pipelines | AWS | CI/CD | Cloud infrastructure | Cost Optimization | Data Pipelines | Drift monitoring | Embeddings | Feature Store | GCP | GPU Utilization | GitHub Actions | Inference Optimization | Infrastructure as Code | Knowledge Distillation | Kubernetes | Language Models | Large Language Models | Latency optimization | MLflow | Machine Learning | Model Drift | Model Serving | Model drift monitoring | Observability | Quantization | RAG | Resource Utilization Monitoring | Resource utilization | Retrieval-Augmented Generation | Serverless computing | Terraform | Throughput Optimization | Vector Database | Vector embeddings | Vector indexing | Vertex AI | Vertex AI pipelines | “as-code”
Education
N/A
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