Compare your current skills to what this opportunity needs—we'll show you what you already have and what could strengthen your application.
Job Description
AI Lead Developer -
Agentic AI & GenAI Location:
Exton, PA Onsite Employment Type:
Full-Time We are seeking a hands-on AI Lead Developer to architect, build, and deploy enterprise-scale Agentic AI, Generative AI, and Multi-Agent Solutions . This role requires strong expertise in AI architecture, LLM-powered applications, cloud-native deployments, and scalable AI platforms. The ideal candidate will lead the development of intelligent AI systems while mentoring engineering teams and driving AI innovation across the organization. Career Growth Opportunity This role is designed to evolve into a leadership position with the opportunity to grow into a Global AI Practice Head , leading AI strategy, delivery, innovation, and capability development across global teams. Key Responsibilities Design and develop enterprise-grade Agentic AI and Generative AI solutions. Build multi-agent systems using LangGraph, AutoGen, ADK, MCP, and related frameworks. Develop RAG-based applications leveraging vector databases and enterprise knowledge sources. Build scalable APIs and AI microservices using Python, FastAPI, and cloud-native architectures. Deploy AI solutions on AWS, Azure, or Google Cloud Platform using Docker, Kubernetes, and CI/CD pipelines. Implement AI security, governance, observability, and guardrails. Collaborate with business, product, and engineering teams to deliver AI-driven solutions. Conduct code reviews, mentor engineers, and contribute to AI architecture and roadmap planning. Required Skills 10+ years of software engineering experience with 4+ years in AI/GenAI solutions. Strong Python development experience with FastAPI or Flask.
Hands-on experience with:
LangChain LangGraph AutoGen ADK MCP (Model Context Protocol) Multi-Agent Architectures Experience building RAG pipelines, embeddings, prompt engineering, and vector search solutions. Experience with vector databases such as Pinecone, Weaviate, FAISS, Chroma, or Azure AI Search. Strong knowledge of REST APIs, microservices, distributed systems, and cloud platforms (AWS/Azure/Google Cloud Platform). Experience with Docker, Kubernetes, Redis, Kafka, and AI application deployment. Strong understanding of AI security, memory management, scalability, and production AI systems. Preferred Skills Experience with OpenAI, Azure OpenAI, Claude, Gemini, and Hugging Face models. Knowledge of MLOps, AI evaluation frameworks, and AI governance. Experience with Spark, Kafka, and large-scale AI platforms. Prior experience leading AI initiatives and mentoring engineering teams.
