AI Engineer (Managed Services)
Tasks
- Apply security guardrails for LLM systems
- Architect LLM applications
- Build LLM model serving infrastructure
- Build RAG systems
- Create evaluation frameworks for LLM quality
- Deploy and optimize open-source LLMs
- Design A B testing for model and prompt iteration
- Develop prompt engineering strategies
- Implement function calling and tool use
- Implement model fine-tuning pipelines
- Implement production monitoring and guardrails
- Integrate MCP for LLM tool interoperability
- Optimize model inference with quantization and batching
- Orchestrate multi-agent workflows
- Track AI research for enterprise adoption
Perks/Benefits
- N/A
Skills/Tech-stack
ARES | AWQ | Agent Orchestration | Agent systems | Attention Mechanisms | AutoAWQ | Autogen | Cache optimization | ChromaDB | Content Moderation | Continuous batching | CrewAI | Cross-Encoder | DORA | DeepEval | Embeddings | FP8 | FastAPI | Fine Tuning | FlashAttention | FlashInfer | Flask | Function Calling | GPTQ | GPU operators | GPU scheduling | Gguf | Haystack | Hybrid search | Hyde | INT8 | Injection Defense | KV cache | KV cache optimization | Kubeflow | Kubernetes | LLM-as-a-Judge | LORA fine tuning | Langchain | Langgraph | Language Models | Large Language Models | Litellm | Llama.cpp | Llamaindex | LoRA | Milvus | Model Context Protocol | Multi-Agent | Multi-Agent Systems | Ollama | PGVector | PII Detection | PageAttention | Pinecone | Prompt engineering | Prompt injection | Prompt injection defense | PyTorch | Python | QLoRA | Qdrant | Quantization | Query Expansion | RAG | RLAIF | RLHF | Ragas | Ray | Reranking | Retrieval-Augmented Generation | SGLang | Speculative decoding | Tensor Parallelism | TensorRT-LLM | Text Generation | Text generation inference | Tool use | Transformer | TrueFoundry | VLLM | Vector Databases | Weaviate | Xinference
Education
Roles
AI | AI Engineer | Engineer | Learning Engineer | Machine Learning Engineer
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