Senior AI Engineer
Tasks
- Architect and manage cloud native AI infrastructure on Google Cloud
- Build and deploy conversational AI agents
- Build data transformation and feature engineering workflows
- Build end-to-end ML pipelines
- Define LLM integration patterns and RAG strategies
- Design build and deploy generative AI applications
- Develop agentic AI workflows and orchestration
- Drive end to end design and delivery of AI modules
- Identify and address technical debt reliability risks and scalability bottlenecks
- Implement CI CD pipelines for ML model serving
- Implement Retrieval-Augmented Generation architectures
- Implement model monitoring, drift detection, and retraining
- Lead design reviews and establish engineering standards
- Optimize prompt engineering strategies
- Set up monitoring observability and alerting for AI ML workloads
Perks/Benefits
- N/A
Skills/Tech-stack
AI Search | Agent Builder | Agent Engine | Artifact Registry | BigQuery | BigQuery ML | CI/CD | Cloud Build | Cloud Functions | Cloud Monitoring | Cloud Run | Cloud Storage | Cloud logging | Cloud platform | Content Safety | Data Privacy | Data Studio | Dataflow | Dialogflow CX | Drift Detection | Explainability | GKE | Gemini | Google ADK | Google Cloud | Google Cloud Platform | Infrastructure as Code | JAX | Kubeflow | Langchain | Llamaindex | Looker | MLflow | Model Fairness | Model Monitoring | Model Registry | Pub/Sub | Python | RAG Pipelines | Retrieval-Augmented Generation | SQL | TensorFlow | Terraform | Vector Search | Vertex AI | Vertex AI Model Registry | Vertex AI Search | “as-code”
Education
Roles
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