AI Engineer
Tasks
- Build AI agent layer for proactive nudges and smart suggestions
- Build multi model routing scheduling and request prioritization
- Develop tools memory orchestration guardrails and evaluation harnesses
- Implement tensor parallelism quantization and batching
- Instrument AI workloads for performance and usage observability
- Integrate agent features with production APIs
- Optimize inference performance and GPU utilization
- Prototype test and ship product facing features
- Run and optimize self hosted LLM inference stack
- Tune LLM serving for low latency and high throughput
Perks/Benefits
- 401k match
- Health insurance
- Learning and development stipend
- Paid parental leave
- Paid time off
- Remote work flexibility
- Volunteer time off
Skills/Tech-stack
AWQ | AWS | AWS EC2 | Agent Frameworks | CI/CD | Concurrency tuning | Continuous batching | Docker | Embeddings | FP8 | GPTQ | GPU | KV cache | LLM API | Langchain | Langgraph | Language Models | Large Language Models | Monitoring | Multi-LoRa | Observability | 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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