Agentic AI Engineer, AVP
Tasks
- Build evaluation frameworks
- Build production agentic AI solutions
- Create architecture diagrams and runbooks
- Create prompt versioning and context controls
- Design agentic AI platforms
- Develop CI CD deployment workflows
- Develop multi-agent workflows
- Develop tool integration services
- Enforce security and governance controls
- Evaluate emerging models and frameworks
- Implement identity and access management
- Implement observability for LLM apps
- Implement retrieval-augmented generation
- Integrate agent frameworks
- Integrate large language models
- Mentor engineers
- Perform production issue diagnosis
Perks/Benefits
- N/A
Skills/Tech-stack
AI Foundry | APIs | AWS | AWS Bedrock | Access Management | Agent systems | Apache Iceberg | Audit Logging | Authentication | Authorization | Autogen | Automated Evaluation | Automated testing | Azure | Azure AI | Azure AI Foundry | Azure Machine Learning | CI/CD | Cloud Computing | Containerization | Context window | Context window management | Cost monitoring | CrewAI | Data Governance | Databases | Databricks | Distributed Systems | Document parsing | Embeddings | Encryption | GCP | Grounded generation | Human-in-the-loop | Identity & Access | Identity & Access Management | Infrastructure as Code | Java | JavaScript | LLM Tool Calling | Lakehouse | Langchain | Langgraph | Latency monitoring | Llamaindex | Logging | MCP | Machine Learning | Metadata filtering | Model Context Protocol | Model versioning | Monitoring | Multi-Agent | Multi-Agent Systems | Observability | Performance | PostgreSQL | Privacy Compliance | Prompt engineering | Prompt versioning | Python | RAG | REST APIs | Reranking | Resiliency | SageMaker | Scalability | Security | Semantic Kernel | Semantic Memory | Semantic Search | Snowflake | Source Control | Spark | Structured data | The Loop | Token Monitoring | Tool-Calling | TypeScript | Vector Databases | Vector Search | Window management | Workflow Orchestration | “as-code”
Education
Roles
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