Senior AI Engineer
CAD 145K-180K (estimate) Senior-level Full Time
Tasks
- Architect LLM inference services on Azure
- Architect agentic multi agent orchestration
- Architect production LLM applications
- Automate regression testing for LLM outputs
- Build advanced RAG pipelines
- Build data pipelines for LLM systems
- Build memory and state persistence
- Create LLM evaluation frameworks
- Create technical documentation
- Design human-in-the-loop workflows
- Design prompt engineering strategy
- Design structured extraction
- Develop dialogue systems
- Establish guardrails and hallucination mitigation
- Implement AI system monitoring and drift detection
- Implement LLMOps practices
- Implement audit trails for agent actions
- Implement function calling for tool use
- Implement hybrid retrieval architectures
- Implement multi-document reasoning
- Implement planning and tool use
- Integrate vector databases
- Lead LLM model selection
- Lead design and code reviews
- Mentor engineers and lead knowledge sharing
- Select and integrate embedding models
- Set agent observability and monitoring
- Set up CI CD for AI pipelines
- Translate requirements into AI initiatives
- Write modular production code
Perks/Benefits
- N/A
Skills/Tech-stack
A/B | A/B Testing | AI Search | Agent Orchestration | Audit Logging | Autogen | Azure AI | Azure AI Search | Azure ML | Azure OpenAI | B testing | CI/CD | Cost Optimization | CrewAI | Docker | Embeddings | Experiment tracking | Function Calling | Guardrails | Hallucination mitigation | Human-in-the-loop | Hybrid retrieval | Kubernetes | LLM Evaluation | Langchain | Langgraph | Language Models | Large Language Models | Latency optimization | MLOps | MLflow | Monitoring | Multi-Agent | Multi-agent orchestration | Observability | Prompt engineering | Python | RAG | Regression testing | The Loop | Tool-Calling | Trace Logging | Vector Databases | Weights and Biases
Education
Bachelor of Engineering | Bachelor of Science | Master of Science
Roles
Regions
Countries
States
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