
Innovative AI Architectural Designer
10 hours ago
At our company, we are seeking a skilled AI Engineer to play a pivotal role in shaping and scaling our innovative AI Agent Framework.
This is an exciting hands-on engineering opportunity that focuses on designing, implementing, and integrating intelligent, modular agent systems capable of reasoning, planning, and interacting autonomously in real-world applications.
Key Responsibilities:
- Design and implement agent-oriented systems supporting task decomposition, memory handling, and contextual planning.
- Develop and extend our in-house AI Agent Framework with reusable components like tools, memory modules, and orchestration logic.
- Build and integrate vector search, semantic retrieval, and memory systems to enable long-term, goal-driven agent behavior.
- Connect LLMs (e.g., OpenAI, Anthropic) and NLU/NLP layers to enable natural task inputs and autonomous reasoning capabilities.
- Develop RESTful APIs using FastAPI, Flask, or Django to expose agent capabilities and integrate with other products.
- Design scalable, event-driven microservice architectures tailored for AI-native workloads and agent frameworks.
- Prioritize responsible AI design with user safety, transparency, and privacy at the core.
Required Skills & Qualifications:
- Education: Bachelor's or Master's degree in Computer Science, Artificial Intelligence, or a related field.
- Python Proficiency: Strong software engineering skills in Python (3.x) with deep understanding of async programming, architecture design, and typing.
- AI/LLM Experience: 3+ years of practical experience using LLMs for reasoning, prompt engineering, chaining, or building agent-like applications.
- Frameworks & Tools: FastAPI, Flask, or Django; LangChain, LlamaIndex, or other agent frameworks; Vector stores (e.g., FAISS, Weaviate, Pinecone); PostgreSQL, Redis, and event queues (e.g., Celery, RabbitMQ).
- Version Control & Testing: Proficient with Git workflows and writing tests using Pytest or similar.
Bonus Skills (Nice-to-Have):
- Experience with autonomous agents, tool-calling, or memory-driven task systems.
- Familiarity with cognitive architectures or symbolic reasoning.
- Contributions to open-source AI frameworks or tooling.
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