AI Engineer
Tasks
- Build and orchestrate agent workflows
- Build evaluation frameworks for model quality
- Collaborate to deliver end to end AI solutions
- Deploy and manage models with Vertex AI or Azure AI Foundry
- Design and develop LLM powered applications and AI agents
- Design and optimize RAG pipelines
- Ensure observability with traces metrics and logs
- Implement chunking strategies
- Implement guardrails for LLM safety
- Implement human-in-the-loop systems
- Implement retrieval strategies
- Integrate Model Context Protocol servers
Perks/Benefits
Skills/Tech-stack
AI Agents | AI Foundry | APIs | Azure | Azure AI | Azure AI Foundry | Benchmarking | Cloud Computing | Containerization | Driven systems | Event Driven | Event Driven Systems | Faiss | GCP | Google Pub/Sub | Hallucination detection | Human-in-the-loop | Hybrid search | Injection Defense | Kafka | Kubernetes | LLM | LLM Evaluation | Langchain | Langgraph | Language Processing | Latency tracking | Logging | MCP | Metrics | Microservices | Model Context Protocol | Natural Language | Natural Language Processing | Observability | Output Validation | Pinecone | Policy enforcement | Prompt injection | Prompt injection defense | Prompt testing | Pub/Sub | Python | RAG | Re-ranking | Regression testing | Retrieval-Augmented Generation | Scalable System Design | System design | The Loop | Token usage | Tool orchestration | Tracing | Vector Databases | Vertex AI | Weaviate
Education
N/A
Roles
AI | AI Engineer | Engineer
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