Agentic AI Engineer
Tasks
- Add guardrails and triage production failures
- Build RAG retrieval pipelines
- Build synthetic data and self improvement loops
- Create offline benchmarks and online evaluation metrics
- Create prompt scaffolds and tool calling specs
- Curate clean and label EDA datasets
- Evaluate LLMs for code and reasoning
- Implement agent tools and memory
- Monitor telemetry and prevent regressions
- Train and fine tune LLMs and embedding models
- Tune knowledge graphs and graph database queries
- Tune vector databases and retrieval accuracy
Perks/Benefits
- N/A
Skills/Tech-stack
Agent Frameworks | ArangoDB | Attention Mechanism | Autogen | Benchmarks | Chroma | DPO | Data cleaning | Data labeling | Distributed Training | Embedding Models | Evaluation | Fine Tuning | GPU Performance | GPU performance tuning | GRPO | Graph Databases | Hugging Face | Hugging Face Transformers | Instruction Tuning | Knowledge graphs | Langgraph | Language Models | Large Language Models | LoRA | NebulaGraph | Neo4j | PEFT | PGVector | Performance Tuning | Preference optimization | Prompt engineering | PyTorch | Python | Qdrant | Quantization | RAG | Retrieval-Augmented Generation | TGI | Telemetry | Tokenization | Tool-Calling | Transformers | Triton | VLLM | Vector Databases | Weaviate
Education
Regions
Countries
States
Cities
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