Senior Machine Learning Engineer
Tasks
- Build agentic workflows
- Build benchmark suites
- Build data pipelines and retrieval layers
- Build multilingual grounding data pipelines
- Build production LLM systems
- Create LLM evaluation harnesses
- Deploy LLM services
- Design RAG pipelines
- Develop orchestration APIs
- Implement automated regression tests
- Implement inference optimization
- Implement request batching
- Implement tool calling systems
- Integrate LLMs into production serving frameworks
- Optimize latency and cost with caching
- Optimize prompt context
- Own end to end backend and AI architecture
- Set up human in the loop validation
- Use model routing
Perks/Benefits
Skills/Tech-stack
Agentic Workflows | Benchmarking | Caching | Cloud infrastructure | Containerization | DPO | Data Pipelines | DeepSpeed | Efficient Serving | Evaluation | FSDP | Human-in-the-loop | Langchain | Langgraph | Language Models | Large Language Models | LoRA | Machine Learning | Model routing | ONNX | ONNX Runtime | Orchestration | Prompt engineering | Python | QLoRA | Quantization | REST API | Regression testing | Request batching | Retrieval-Augmented Generation | SFT | The Loop
Education
N/A
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