Overview
As an AI Engineer specializing in large language models (LLM) and agentic systems, you will be instrumental in designing, developing, and deploying advanced AI solutions that meet real-world business challenges. Collaborating with engineering teams, you will integrate innovative AI capabilities into existing products and processes, contributing to the organization's expansive AI efforts. This role encompasses opportunities across mid-level, senior, and lead positions, allowing for varied levels of responsibility based on your experience and expertise.
Responsibilities
- Design and develop AI agents and agentic workflows that utilize LLMs, RAG, reasoning, memory, and tools to solve complex business challenges.
- Take ownership of delivery throughout the development lifecycle, from exploration and solution design to production deployment and monitoring.
- Build scalable systems using relevant frameworks and architectures, connecting AI capabilities to products and business workflows.
- Collaborate with engineering teams to ensure systems' observability, maintainability, and reliable operation.
- Evaluate design choices based on usability, latency, cost, and performance in real-world scenarios.
- For senior or lead roles, drive projects from inception to completion, ensuring accountability for outcomes and mentoring team members.
Requirements
- Proven experience in delivering production-grade LLM-powered systems, able to articulate technical decisions and outcomes.
- Practical experience with AI agents, tool orchestration, and developing RAG systems that address user needs.
- Strong proficiency in Python with solid foundations in machine learning and production code development.
- Understanding of system design principles and deployment trade-offs for LLM and machine learning solutions.
- Experience with cloud deployment on platforms such as AWS, GCP, or Azure, along with modern software engineering practices.
- Familiarity with MCP-style architectures, FastAPI, LangChain, LangGraph or similar frameworks is advantageous.
- A master's degree or higher in a mathematical, scientific, or computer science discipline is preferred; practical system-building experience is critical.
- For senior and lead positions, demonstrated ownership of projects and ability to mentor colleagues is expected.