AI Engineer
Tasks
- Build document ingestion chunking and embedding
- Build domain knowledge packs
- Design RAG pipelines
- Evaluate answer accuracy and grounding
- Generate cited traceable answers
- Implement data classification and access control
- Implement indexing and retrieval
- Implement orchestration APIs integration
- Integrate knowledge layer with agent runtime
- Onboard document corpora into demo and production packs
- Run quality checks for regulated data
- Tune retrieval quality embeddings re ranking and hybrid search
Perks/Benefits
Skills/Tech-stack
AWS Bedrock | Amazon SageMaker | Azure AI | ChromaDB | Embeddings | GRPC | Gguf | Hugging Face | Hugging Face Transformers | Hybrid search | Knowledge graph | Langchain | Llama.cpp | Llamaindex | MCP | Microservices | Model Context Protocol | OpenSearch | Pinecone | Prompt engineering | PyTorch | Python | Qdrant | REST API | Re-ranking | Retrieval-Augmented Generation | TensorFlow | VLLM | Vector Databases | Vertex AI | WebSocket
Education
Roles
AI | AI Engineer | Engineer | Learning Engineer | Machine Learning Engineer | NLP Engineer
Regions
Countries
States
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