AI Software Developer

Design and build AI capabilities embedded throughout enterprise products, including agentic workflows, retrieval systems, and reasoning engines. . Develop production-grade AI applications using Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), semantic search, and vector databases. . Implement multi-agent orchestration using frameworks such as LangGraph, CrewAI, Semantic Kernel, or Model Context Protocol (MCP). . Build AI capabilities including document intelligence, knowledge retrieval, planning, reasoning, and AI-assisted decision support. . Design and optimize data extraction, classification, embedding, and indexing pipelines. . Integrate AI and LLM services with enterprise APIs, document repositories, and business workflows. . Evaluate and improve model performance, reliability, accuracy, and observability through monitoring, testing, and drift detection. . Partner closely with software engineers, product managers, and architects to deliver scalable AI services through well-designed APIs. . Document AI architecture, prompt engineering strategies, agent workflows, and evaluation methodologies while promoting engineering best practices.

5+ years of software engineering experience, including at least 3 years developing production-grade AI/LLM applications beyond proof-of-concept or notebook development. . Experience developing enterprise B2B SaaS products with embedded AI capabilities. . Hands-on experience with one or more multi-agent orchestration frameworks such as LangGraph, CrewAI, Semantic Kernel, or MCP. . Strong experience designing and implementing Retrieval-Augmented Generation (RAG), vector database, and semantic search solutions. . Advanced Python development experience, including FastAPI or similar frameworks for AI service development. . Experience integrating LLM-based applications with enterprise data sources, APIs, and business workflows. . Strong analytical, problem-solving, and collaboration skills with the ability to communicate technical concepts effectively., Experience building AI-native enterprise products comparable in complexity to Harvey AI, Cursor, Notion AI, Linear, Canva, or similar platforms. . Experience in regulated industries such as Life Sciences, Clinical Research, Healthcare, or Pharmaceuticals. . Experience with Intelligent Document Processing (IDP) solutions such as Azure AI Document Intelligence. . Experience deploying AI solutions on cloud platforms including Azure, AWS, or Google Cloud. . Familiarity with MLOps, AI observability, model evaluation frameworks, and responsible AI practices.