Description

You will design and lead the architecture for multi-agent GenAI systems, ensuring scalability and modularity across the ecosystem.

Responsibilities

• Architect and implement dynamic, goal-oriented agent orchestration frameworks using LangGraph.
• Define the technical roadmap for the GenAI ecosystem, including reference architectures and reusable templates.
• Develop core Python components, integrating APIs, external data sources, and RAG pipelines.
• Lead and mentor developers, enforcing high-quality design, coding standards, and prompt engineering best practices.
• Partner with DevOps to optimize CI/CD pipelines, containerization, and cloud deployment for cost and performance.

Required Skills

• 8+ years of hands-on Python development experience.
• Deep expertise in LLMs, Generative AI, and multi-agent system design.
• Strong proficiency with LangGraph and LangChain for agent orchestration.
• Experience with Azure Cloud services (Azure OpenAI, Azure Vault, AKS, App Insights).
• Solid understanding of RAG architectures, vector stores, embeddings, and document parsing.
• Proficiency in containerization (Docker) and orchestration (Kubernetes) for production AI workloads.
• Experience with CI/CD workflows, specifically GitHub Actions.
• Knowledge of Agentic AI protocols (MCP, A2A) and agent observability.
• Strong system design skills, including microservices, API architecture, and secure data flow.

Preferred Skills

• Experience with MLOps/LLMOps frameworks for model management and evaluation.
• Familiarity with Azure AI Studio and Prompt Flow.
• Background in implementing Agent Factory ecosystems for enterprise GenAI.

Key Skills

Education

Any Graduate