What are the responsibilities and job description for the Senior AI Software Engineer (Agentic Systems & LLM Applications) | W2 Only | REMOTE | position at Xlysi?
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