Fullstack Python (AI Agents, LangGraph, Context Engineering)

Position Overview:

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, Google Cloud Platform, 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.