AI Engineer
Tasks
- Build tools memory orchestration guardrails and evaluation harnesses
- Implement multi LoRA routing and request scheduling
- Improve concurrency and continuous batching
- Instrument AI workloads for performance and usage visibility
- Integrate agent features with production APIs
- Integrate observability and monitoring
- Manage KV cache and prefix caching
- Optimize tensor parallelism and quantization
- Run and optimize self hosted LLM inference stack
- Ship in app agent layer for proactive nudges and summaries
- Test prototypes with fast UI iterations
- Tune inference serving for low latency and high throughput
Perks/Benefits
Skills/Tech-stack
AWQ | AWS | Agent SDK | CI/CD | CUDA | Claude Agent SDK | Continuous batching | Docker | Embeddings | FP8 | GPTQ | Inference Optimization | KV cache | LLM APIs | Langgraph | Monitoring | Multi-LoRa | Observability | Prefix caching | Python | Quantization | RAG | Request Scheduling | Routing | SGLang | Speculative decoding | Tensor Parallelism | TensorRT-LLM | VLLM
Education
N/A
Roles
AI | AI Engineer | Engineer | Learning Engineer | Machine Learning Engineer
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