Conversational AI Engineer
Tasks
- Architect agentic AI workflows
- Build RAG solutions
- Build SDKs and APIs
- Create caching strategies
- Define prompt engineering standards
- Design conversational AI applications
- Detect hallucinations
- Develop and deploy LLM powered systems
- Develop developer tooling
- Develop evaluation frameworks
- Ensure AI security and governance
- Evaluate emerging LLMs and frameworks
- Implement AI safety and guardrails
- Implement inference efficiency
- Implement model monitoring
- Implement multi-agent systems
- Mentor engineers and set engineering standards
- Optimize LLM performance
- Orchestrate AI tools
Perks/Benefits
- N/A
Skills/Tech-stack
AI Governance | AWS | Agent systems | Agentic AI | Autogen | Azure | CI/CD | Caching | CrewAI | Embeddings | Evaluation Frameworks | FedRAMP | Google Cloud | HIPAA | Hallucination detection | Kubernetes | LLM Inference | Langchain | Langgraph | Language Models | Large Language Models | Llamaindex | MLOps | Model Monitoring | Multi-Agent | Multi-Agent Systems | Observability | PCI DSS | Prompt engineering | Python | RAG | Responsible AI | Retrieval-Augmented Generation | SOC 2 | Semantic Kernel | Tool orchestration | Vector Databases
Education
Bachelor of Engineering | Bachelor of Science | Master of Science
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