Senior AI Software Engineer (Agentic Systems & LLM Applications) | W2 Only | REMOTE |

Role Overview We are seeking an experienced AI‑focused Software Engineer to design, build, and scale intelligent applications powered by modern AI and large language models. This is not a Data Scientist role—we are looking for a strong software engineering professional with deep expertise in building production‑grade AI systems, APIs, and distributed architectures. Key Responsibilities Design and develop Python‑based APIs for AI‑powered applications and services Build and orchestrate agentic workflows using modern frameworks such as LangChain, LangGraph, AutoGen, Semantic Kernel, or LlamaIndex Implement and optimize Retrieval‑Augmented Generation (RAG) pipelines Develop scalable AI systems leveraging machine learning and deep learning frameworks Integrate and manage vector databases (e.g., Pinecone, Weaviate, Chroma) for semantic search and memory Architect and deploy applications across cloud platforms (AWS, GCP, Azure) Apply domain‑driven design (DDD) principles and build microservices architectures Ensure code quality through strong use of object‑oriented programming principles (inheritance, polymorphism) and proven design patterns Collaborate cross‑functionally to deliver robust, production‑ready AI solutions Required Qualifications Strong experience in Python software development Hands‑on experience with LLM frameworks and orchestration tools (LangChain, LangGraph, AutoGen, etc.) Solid understanding of RAG architectures and vector search systems Experience working with machine learning frameworks such as TensorFlow or PyTorch Proficiency in building and consuming RESTful APIs Experience with cloud infrastructure (AWS, GCP, or Azure) Strong knowledge of microservices architecture and domain‑driven design Deep understanding of object‑oriented programming concepts and software design patterns Preferred Qualifications Experience building agent‑based or autonomous AI systems Familiarity with real‑time AI applications and streaming architectures Experience optimizing AI systems for performance and scalability Exposure to MLOps practices and deployment pipelines What Success Looks Like You deliver scalable, production‑grade AI systems, not prototypes You can design complex agentic workflows that solve real business problems You write clean, maintainable, and well‑architected code You bridge the gap between AI capabilities and software engineering excellence #J-18808-Ljbffr Xlysi LLC.