Sr. Applied AI Engineer
Tasks
- Benchmark models prompts and approaches
- Build and evaluate production AI applications
- Collaborate in agile sprint environment
- Define metrics and gather performance data
- Deploy LLM-driven systems
- Design develop and deploy evaluation pipelines
- Develop agentic LLM workflows
- Extend test suites and run benchmarks
- Optimize cost latency and accuracy
- Participate in AI product lifecycle from experimentation to deployment
- Plan experiments and execute autonomously
Perks/Benefits
- Ergonomic workplace
- Learning resources
- Meal allowance
- Mental health benefit
- Paid time off
- Private health insurance
- Professional development grant
- Remote work option
Skills/Tech-stack
Agent Orchestration | Asynchronous services | Benchmarking | Caching | Context compression | Evaluation Pipelines | Fine Tuning | High concurrency | LLM Engineering | Langgraph | Language Models | Large Language Models | MCP | Model Cascading | Model Distillation | Prompt Compression | Prompt engineering | Python | RAG | Synthetic test data | Test Data | Tool-Calling
Education
N/A
Roles
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