AI Engineer (Managed Services)
Tasks
- Architect and build production LLM applications
- Build AI agent and multi agent systems
- Build model serving infrastructure
- Create evaluation frameworks for LLMs
- Deploy and optimize open-source LLMs
- Design A B testing for models and prompts
- Design and implement RAG systems
- Develop prompt engineering strategies
- Implement LLM security guardrails
- Implement fine-tuning pipelines
- Implement function calling and tool use
- Implement model inference optimization
- Implement production monitoring for LLM systems
- Integrate MCP for LLM tool interoperability
- Orchestrate multi-agent workflows
Perks/Benefits
- N/A
Skills/Tech-stack
A/B | A/B Testing | AWQ | AutoAWQ | B testing | Cache optimization | ChromaDB | Continuous batching | DORA | DeepEval | Embeddings | FP8 | FastAPI | FlashAttention | FlashInfer | GPTQ | Gguf | Guardrails | Haystack | Hybrid search | Hyde | INT8 | Inference Optimization | Injection Defense | KV cache | KV cache optimization | Kubernetes | LLM Evaluation | Langchain | Language Models | Large Language Models | Latency optimization | Llama.cpp | Llamaindex | LoRA | Milvus | Model Monitoring | Ollama | PGVector | PageAttention | Pinecone | Prompt engineering | Prompt injection | Prompt injection defense | PyTorch | Python | QLoRA | Qdrant | Quantization | Query Expansion | RAG | Ragas | Reranking | SGLang | Speculative decoding | Tensor Parallelism | TensorRT-LLM | Text Generation | Text generation inference | Transformers | VLLM | Vector Databases | Weaviate | Xinference
Education
Roles
AI | AI Engineer | Engineer | Learning Engineer | Machine Learning Engineer
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