Join our team as an Agentic AI Senior Lead/Developer to architect, build, and launch cutting-edge AI systems. This role centers on leveraging autonomous agents, large language models (LLMs), and modern cloud infrastructures. You'll utilize your expertise in Python, AWS, and Azure to create robust, scalable AI solutions. This position offers a dynamic blend of hands-on coding, strategic architectural design, and technical leadership in delivering sophisticated autonomous and multi-agent systems for diverse enterprise applications.

Design and implement advanced agent-based AI systems using popular frameworks like LangChain, LangGraph, AutoGen, or CrewAI. Develop sophisticated multi-agent orchestration systems featuring memory, planning, and tool utilization. Integrate LLMs from various providers, including OpenAI and Azure OpenAI, into enterprise workflows. Implement Retrieval-Augmented Generation (RAG) pipelines with vector databases and build secure, production-ready prompt engineering frameworks. Develop scalable Python backend services with frameworks such as FastAPI, Flask, or Django, and build AI microservices for seamless enterprise integration. Implement async processing, workflow automation, and event-driven architectures, ensuring code quality through unit testing and CI/CD practices. Design and deploy AI solutions on AWS (SageMaker, Bedrock, Lambda) and Azure (Azure OpenAI, Azure ML, AKS), employing infrastructure as code principles. Establish CI/CD pipelines, manage containerization with Docker and Kubernetes, and implement model monitoring and governance for responsible AI practices. Lead AI architecture discussions, mentor junior engineers, and contribute to AI strategy and roadmap development, collaborating closely with cross-functional teams.

We are seeking candidates with strong Python expertise, ideally with 8+ years of experience. A solid understanding of LLMs and agent frameworks is essential, alongside deep knowledge of prompt engineering, RAG architecture, and vector databases such as Pinecone, FAISS, Weaviate, or Azure AI Search. Proficiency in integrating APIs and extensive experience with AWS and Azure cloud services are required. Familiarity with REST APIs, microservices, event-driven architectures, Git, CI/CD, Docker, and Kubernetes is crucial. Experience deploying AI systems in production, coupled with an understanding of AI governance and model monitoring, is expected. Experience integrating enterprise data sources like SharePoint, SAP, or CRM systems is a significant advantage.