Senior AI Engineer
USD 135K-230K (estimate) Senior-level Full Time
Tasks
- Architect multi agent orchestration
- Architect production LLM capabilities
- Build RAG pipelines
- Build data pipelines
- Create prompt engineering strategies
- Create runbooks model cards and evaluation playbooks
- Define LLM evaluation frameworks
- Design dialogue systems
- Design human in the loop approval workflows
- Develop LLM inference services on Azure
- Develop multi document reasoning systems
- Establish monitoring for latency quality and safety
- Implement LLMOps practices
- Implement guardrails and hallucination mitigation
- Implement structured extraction
- Implement tool use and function calling
- Integrate embedding models and vector databases
- Maintain architecture decision records
- Mentor engineers through design reviews and knowledge sharing
- Select and integrate frontier and open source models
- Set up audit trails and trace logging
- Set up automated regression testing for LLM outputs
- Translate requirements into scoped AI initiatives
Perks/Benefits
- N/A
Skills/Tech-stack
A/B | A/B Testing | AI Search | Agent systems | Anthropic API | Audit Logging | Autogen | Azure AI | Azure AI Search | Azure Machine Learning | Azure OpenAI | B testing | CI/CD | Claude | CrewAI | DeepEval | Docker | Embedding Models | Fine Tuning | Function Calling | Google Gemini | Human-in-the-loop | Kubernetes | LLMOps | Langchain | Langgraph | Latency optimization | Llama | LoRA | MLflow | Machine Learning | Mistral | Monitoring | Multi-Agent | Multi-Agent Systems | Observability | OpenAI API | Pinecone | Prompt engineering | Python | Q LoRA | RAG | Ragas | The Loop | Tool use | TruLens | Vector Databases | Weaviate | Weights and Biases
Education
N/A
Roles
Regions
Countries
States
Cities
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