AI Engineer
Tasks
- Apply prompt engineering best practices
- Build LLM pipelines
- Design AI architecture for scalability and reliability
- Design data ingestion and preprocessing pipelines
- Ensure production readiness with testing CI CD containerisation and monitoring
- Implement LLM observability and performance metrics
- Implement classification routing and decision workflows
- Implement structured outputs and human in the loop workflows
- Integrate AI solutions into existing workflows
- Monitor evaluate and optimize AI systems
Perks/Benefits
Skills/Tech-stack
Async Programming | Async workers | Azure Container | Azure Container Apps | Azure Service | Azure Service Bus | CI/CD | Container Apps | Containerization | Distributed Systems | Docker | Event Driven | Event-driven architecture | FastAPI | Human-in-the-loop | IAM | Infrastructure as Code | KEDA | Kafka | LLM Evaluation | LLM Observability | Langchain | Langgraph | Monitoring | OpenAI | PostgreSQL | Prompt engineering | Python | REST APIs | RabbitMQ | Secrets management | Service Bus | Structured outputs | Terraform | Testing | The Loop | “as-code”
Education
N/A
Roles
AI | AI Engineer | Engineer
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