AI Agent Engineer

Responsibilities:

  • Architecting conversational + autonomous agents using LLMs.
  • Building context and memory layers, embeddings, and retrieval pipelines.
  • Implementing RAG frameworks and vector-based search.
  • Setting up MCP servers and autonomous tool orchestration.
  • Improving hallucination reduction, safety, and evaluation pipelines.
  • Integrating multimodality across text, voice, image, and video.
  • Designing internal AI Agent SDKs and reusable components.
  • Experimenting with the latest LLMs, APIs, and model fine-tuning.
  • Supporting telemetry, A/B testing, and performance analytics.


Requirements:

  • Strong experience building LLM apps, chatbots, or agentic systems.
  • Deep understanding of LLM architecture, embeddings, memory, and context.
  • Hands-on experience with LangChain / LlamaIndex.
  • Strong Python development skills.
  • Experience with RAG pipelines and vector search.
  • Vector DBs: Pinecone / Qdrant / ChromaDB / FAISS.
  • Experience in FastAPI / Flask.
  • Integration with OpenAI / HuggingFace APIs.
  • Solid debugging and agent evaluation skills.
  • Understanding of tool use, orchestration, or function calling.
  • MCP server setup and autonomous tool orchestration.
  • Knowledge Graphs, multi-agent frameworks.
  • Voice agents, STT, TTS, emotion detection APIs.
  • Cloud: AWS/GCP/Azure, CI/CD (GitHub Actions, Jenkins).
  • Weights and Biases, LangFuse, PromptLayer.
  • Experience contributing to AI infrastructure or internal SDKs.
  • Familiarity with multimodal models.
  • EdTech experience (not mandatory).
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