AI Engineer (Managed Services)
Tasks
- Architect LLM applications
- Build LLM evaluation frameworks
- Build RAG systems
- Deploy and optimize open-source LLMs
- Design A B testing for models
- Develop model fine-tuning pipelines
- Implement model quantization and inference optimization
- Implement prompt engineering strategies
- Integrate tool calling for agents
- Orchestrate multi-agent workflows
- Set up production monitoring and guardrails
- Tune retrieval reranking and hybrid search
Perks/Benefits
- N/A
Skills/Tech-stack
A/B | A/B Testing | AWQ | AutoAWQ | B testing | Chain-of-Thought | ChromaDB | Content Moderation | DORA | DeepEval | Embedding Models | FP8 | FastAPI | FlashAttention | Flask | GPTQ | Gguf | Haystack | Hybrid search | INT8 | Injection Defense | JAX | KV cache | Kubernetes | LLM-as-a-Judge | Langchain | Language Models | Large Language Models | Llama.cpp | Llamaindex | LoRA | Milvus | Ollama | Output parsing | PGVector | PII Detection | Pinecone | Prompt injection | Prompt injection defense | PyTorch | Python | QLoRA | Qdrant | Query Expansion | Ragas | Reranking | Retrieval-Augmented Generation | SGLang | Structured Output | Structured output parsing | TGI | TensorFlow | TensorRT-LLM | Text Generation | Text generation inference | Tree of Thought | VLLM | Vector Database | Weaviate | Xinference
Education
Roles
AI | AI Engineer | Engineer | Learning Engineer | Machine Learning Engineer
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