FullStack Python (AI Agents, LangGraph, Context Engineering)

Job Title Integration Engineer – FullStack Python (AI Agents, LangGraph, Context Engineering) Location – 1st Atlanta, GA 2nd Dallas, TX 3rd Seattle, WA (Onsite) Looking for 12+ years Description: • We are seeking a talented and driven Integration Engineer with a strong FullStack Python background to join our AI solutions team. • In this role, you will be responsible for building, integrating, and optimizing intelligent systems leveraging AI agents, the LangGraph framework, and advanced context engineering techniques. • You will work across the stack, collaborating with data scientists, ML engineers, and product teams to deliver scalable, adaptive, and context-aware solutions. 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.