About this role
The Intermediate AI Engineer will design, develop, and deploy cutting-edge AI systems, focusing on Large Language Models (LLMs), chatbots, Retrieval-Augmented Generation (RAG), and agentic AI architectures. This role involves hands-on development of multi-agent systems and enterprise-grade AI platforms. Responsibilities include: Agentic AI Development: Designing and deploying multi-agent systems for autonomous decision-making and reasoning using frameworks like LangChain and LangGraph. RAG Pipelines: Implementing and optimizing RAG systems to ensure grounded, accurate responses. LLM Engineering: Fine-tuning and prompt-engineering LLMs for task-specific reasoning and dynamic adaptation. Enterprise AI Platform: Leading the development of platforms integrating LLMs, RAG, and Model Context Protocol (MCP). MLOps & Observability: Establishing best practices for monitoring and maintaining scalable AI solutions. Applied AI Prototyping: Rapidly prototyping and iterating on AI agent capabilities. Collaboration & Research: Participating in the full research cycle and collaborating with cross-functional teams. Required Qualifications: Bachelor’s degree in Computer Science, Engineering, or a related quantitative field. 5+ years of overall experience in software development, data science, or machine learning. 1+ year of hands-on experience with LLMs, retrieval-based methods, fine-tuning, or agent-based architectures. 1+ year of experience with frameworks like LangChain, LlamaIndex, or OpenAI. Strong programming skills in Python and basic SQL. Experience deploying on Google Cloud Platform (GCP) with Vertex AI and IBM Watsonx. Familiarity with agentic AI protocols and Agent Development Kits (ADKs). Preferred Qualifications: Experience with Model Context Protocol (MCP), LangGraph, or AutoGen. Knowledge of MLOps best practices, responsible AI principles, and enterprise-scale deployments. Contributions to open-source AI/ML projects are a plus. Master’s or Ph.D. is a strong plus.