Senior Applied AI Engineer
Tasks
- Build data pipelines for models and product surfaces
- Collaborate with backend on service integrations
- Define multi tenant isolation and auditing
- Design AI systems in production end to end
- Design evaluation frameworks for quality and regression detection
- Implement AI safety guardrails including human approvals
- Implement output quality control
- Integrate LLMs in production including prompt engineering and tool use
- Manage LLM cost and latency
- Mentor engineers on applied AI practices
- Select solution type using evaluation
- Translate ambiguous product problems into measurable solutions
Perks/Benefits
Skills/Tech-stack
API Integration | Artificial Intelligence | Batch Processing | Data Pipelines | Distributed tracing | Docker | Drift Detection | Evaluation metrics | Event Driven | Event-driven architecture | Fine Tuning | Function Calling | Go | JAX | Kafka | Kubernetes | LLM | Machine Learning | Metrics | Model Drift | Model Drift Detection | Model selection | Multi-tenant | Multi-tenant architecture | Node.js | Observability | Prompt engineering | Pub/Sub | PyTorch | Python | RAG | Retrieval-Augmented Generation | Rust | Scikit-learn | Security auditing | Tenant architecture | Tool use | TypeScript
Education
N/A
Regions
Countries
States
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