AI Engineer
Indexed description
Key Responsibilities
- Design, build, and deploy small-scale data extraction agents that leverage LLMs for structured information retrieval and summarization.
- Develop agentic strategies for joining structured and unscheduled data.
- Implement prompt-chaining and multi-agent orchestration frameworks to support adaptive, context-aware workflows.
- Develop and maintain “LLM-as-judge” evaluation loops for quality assurance, safety checks, and decision arbitration among autonomous agents.
- Contribute to the design of agent governance frameworks, including policy enforcement, alignment scoring, and auditing mechanisms.
- Collaborate with data engineers, ML researchers, and product teams to integrate agents into pipelines and user interactions.
- Monitor, evaluate, and iterate on model performance, latency, and reliability metrics in real-world use cases.
- Write clear, maintainable code and documentation for agent design and use.
- Stay current with advances in LLM architectures, tool-use capabilities, and AI governance trends.
- Master Degree or Bachelor (with experience) in Computer Science or a related field
- 2–3 years of hands-on experience building or fine-tuning NLP/LLM systems or AI-driven automation workflows.
- Proficiency in Python and experience with frameworks like LangChain, LlamaIndex, or OpenAI SDKs.
- Familiarity with vector databases (e.g., Pinecone, Weaviate, FAISS) and retrieval-augmented generation (RAG) techniques.
- Understanding of LLM-as-judge methodologies (e.g., self-evaluation, pairwise scoring, and preference modeling).
- Experience with multi-agent systems, task routing, or agent governance design is strongly preferred.
- Strong analytical, communication, and documentation skills; comfortable translating research insights into practical engineering solutions.
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