Overview
The AI Engineer for LLM & Agentic Systems will be instrumental in developing and deploying AI-powered solutions focused on agentic workflows and language model integration for a leading data and information services company. The role involves collaborating with engineering teams to build robust systems that tackle complex business challenges while ensuring real-world usability. This position is well-suited for professionals with a proven track record of delivering production-grade AI applications and a strong understanding of the technical intricacies involved in such projects.
Responsibilities
- Design and build AI agents and agentic workflows utilizing LLMs and related technologies.
- Own the delivery process from exploration to production deployment, ensuring seamless integration and iteration.
- Develop reliable and scalable systems using frameworks such as FastMCP/FastAPI and LangChain/LangGraph.
- Integrate AI capabilities into core products, APIs, and business workflows, focusing on efficiency and user experience.
- Collaborate with cross-functional engineering teams to ensure maintainability and observability in system architectures.
- Leverage cloud deployment environments like AWS, GCP, or Azure to implement modern engineering practices.
- Mentor team members and contribute to the technical growth of the organization by sharing knowledge and best practices.
Requirements
- Proven experience shipping LLM-powered systems with documented real-world applications.
- Hands-on knowledge of building AI agents, including orchestration and multi-step reasoning capabilities.
- Strong proficiency in Python and solid foundations in machine learning principles and system design.
- Experience with cloud deployment on platforms such as AWS, GCP, or Azure.
- Demonstrated product mindset with a focus on system impact and user adoption rather than solely on model performance.
- Educational background with a Master's degree or higher in mathematics, science, or computer science is preferred.
- A history of mentoring others and taking ownership of end-to-end workflows and projects in AI or SaaS environments.