Sr. Applied AI Engineer
Tasks
- Benchmark models prompts and approaches
- Build evaluation pipelines
- Collect data to improve AI performance
- Continuously improve AI systems
- Define performance metrics
- Deploy LLM-driven systems
- Design agent orchestration workflows
- Develop agentic flows with LLMs
- Extend test suites and benchmarks
- Implement tool calling systems
- Optimize cost and latency
- Plan experiments and execute tasks autonomously
Perks/Benefits
Skills/Tech-stack
Agent Orchestration | Agentic loops | Asynchronous services | Benchmarking | Caching | Context compression | Cost Optimization | Distillation | Evaluation | Fine Tuning | High concurrency | LLM Engineering | Langgraph | Language Models | Large Language Models | Latency optimization | MCP servers | Model Cascading | Prompt Compression | Prompt engineering | Python | RAG | Retrieval-Augmented Generation | Synthetic data | Test automation | Tool-Calling
Education
N/A
Roles
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