Senior AI Engineer
Tasks
- Architect and manage cloud native AI infrastructure on Google Cloud
- Build and maintain agentic AI workflows and orchestration
- Build scalable data transformation and feature engineering workflows
- Champion responsible AI practices
- Design and implement conversational AI agents
- Design build and deploy generative AI applications
- Design secure scalable and cost optimized GCP environments
- Develop custom machine-learning models
- Develop end-to-end machine learning pipelines
- Develop multimodal AI capabilities
- Implement CI CD pipelines for ML model serving
- Implement Retrieval-Augmented Generation architectures
- Implement model monitoring, drift detection, and retraining
- Mentor junior engineers
- Optimize prompt engineering and grounding mechanisms
- Produce technical documentation and runbooks
- Set up monitoring observability and alerting for AI workloads
Perks/Benefits
- N/A
Skills/Tech-stack
AI Pipelines | Agent Builder | Artifact Registry | Automated retraining | BigQuery | BigQuery ML | CI/CD | Cloud Build | Cloud Deployment Manager | Cloud Functions | Cloud Monitoring | Cloud Run | Cloud Storage | Cloud deployment | Cloud logging | Dataflow | Deployment Manager | Dialogflow CX | Drift Detection | Embedding pipelines | GKE | Gemini | Google ADK | Google Agent Builder | Infrastructure as Code | JAX | Kubeflow | Langchain | Llamaindex | Looker | Model Monitoring | Model Registry | PaLM 2 | Prompt engineering | Pub/Sub | PyTorch | Python | RAG | Responsible AI | Retrieval-Augmented Generation | SQL | TensorFlow | Terraform | Vector Search | Vertex AI | Vertex AI Model Registry | Vertex AI pipelines | “as-code”
Education
Roles
Regions
Countries
States
Cities
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