Senior AI Engineer
Tasks
- Architect retrieval strategies
- Build AI agents and workflows
- Build evaluation harnesses
- Continuously improve LLM evaluations
- Deploy services with Docker and Kubernetes
- Design LLM-powered applications
- Implement context engineering
- Integrate LLMs with APIs and data stores
- Integrate vector databases for RAG
- Monitor and observe LLM workflows
- Operate LLM systems in production
Perks/Benefits
- N/A
Skills/Tech-stack
AWS | Agents | Attention Mechanism | Azure | Bash Scripting | Context engineering | Distillation | Distributed Systems | Docker | Docker manifests | DynamoDB | Embeddings | Evaluation | Fine Tuning | GCP | GRPC | Haystack | IAM | Kubernetes | LLM orchestration | Langchain | Langfuse | Langgraph | Langsmith | Language Models | Large Language Models | Linux | Llamaindex | Low Latency | Low Latency Inference | Model Context Protocol | MongoDB | MySQL | PGVector | Pinecone | PostgreSQL | Pydantic | Pydantic AI | Python | RAG | REST APIs | Redis | Retrieval-Augmented Generation | Sampling | Streaming | Tokenization | Tool use | Vector Databases | Weaviate
Education
N/A
Roles
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