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About the role
Key Responsibilities: Fullstack Development: Design, develop, and maintain end-to-end Python applications that interface with AI agents and backend services, ensuring robust, scalable, and maintainable codebases. AI Agent Integration: Implement and orchestrate autonomous and semi-autonomous AI agents, connecting them with APIs, data sources, and user-facing interfaces. LangGraph Utilization: Leverage the LangGraph framework to construct, visualize, and manage complex conversational flows and agent interactions. Context Engineering: Architect and implement systems for dynamic context management, memory, and prompt engineering to optimize agent behavior and user experience. System Integration: Integrate machine learning models, vector databases, and third-party services, ensuring seamless interoperability across components. Collaboration: Work closely with cross-functional teams to define requirements, propose technical solutions, and drive successful project delivery. Testing & Optimization: Develop automated tests, monitor system performance, and continually refine integration points for reliability and efficiency. Required Skills & Qualifications: Proven experience with fullstack Python development (FastAPI, Flask, Django, React/Vue/Angular, SQL/NoSQL databases). Hands-on experience building and integrating AI agents (LLMs, RAG, multi-agent systems) in production environments. Familiarity with the LangGraph framework and agent orchestration patterns. Deep understanding of context engineering, including retrieval-augmented generation, prompt design, and session management. Experience with cloud platforms (AWS, GCP, Azure), containerization (Docker), and CI/CD pipelines. Strong problem-solving skills, attention to detail, and a collaborative mindset. Preferred: Knowledge of advanced ML model serving, vector search, or knowledge graph integration. Experience with modern front-end frameworks and data visualization tools. Contributions to open-source AI or agent frameworks.
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