Conversational AI Engineer
Tasks
- Architect and implement agentic AI workflows multi agent systems and tool orchestration
- Build evaluation frameworks for prompt quality hallucination detection and response accuracy
- Define prompt engineering standards and reusable prompt libraries
- Design develop and deploy enterprise conversational AI applications
- Develop reusable SDKs APIs and developer tooling for AI application development
- Evaluate emerging LLMs agent frameworks and AI technologies
- Implement AI safety Responsible AI security compliance guardrails and governance practices
- Mentor engineers and establish engineering standards for architecture testing deployment and monitoring
- Optimize LLM performance inference efficiency prompt execution caching and scalability
Perks/Benefits
Skills/Tech-stack
AI Evaluation | AI Governance | APIs | AWS | Agent systems | Agentic AI | Autogen | Azure | CI/CD | Caching | Cloud platform | CrewAI | Embeddings | FedRAMP | Google Cloud | Google Cloud Platform | Guardrails | HIPAA | Inference Optimization | Kubernetes | 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 | SDKs | SOC 2 | Security | Semantic Kernel | Tool orchestration | Vector Databases
Education
Roles
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