Backend AI Engineer

Responsibilities:

• System Ownership: Design, develop, and own scalable backend services and high-performance APIs for enterprise AI applications.
• Production-Grade AI Systems: Develop robust backend architectures integrating large language models, vector databases, retrieval systems, and external APIs.
• Agent and RAG Infrastructure: Build and optimize backend infrastructure powering AI agents, RAG pipelines, and enterprise automation workflows.
• Architecture and Reliability: Make key architectural decisions, troubleshoot complex technical challenges, and continuously improve system scalability, monitoring, and performance.
• Leadership and Mentorship: Lead technical discussions, collaborate across AI/ML and product teams, and mentor junior engineers while fostering engineering excellence.

Requirements:

• Senior Backend Expertise: 5+ years of backend software engineering experience with strong proficiency in Python and modern frameworks like FastAPI, Flask, or Django.
• System Design and Databases: Deep understanding of database design, data modeling, microservices, and distributed systems architecture.
• Generative AI Systems: Hands-on experience building generative AI applications, LLM integrations, RAG architectures, or agentic frameworks.
• Engineering Standards: Proven track record of writing clean, maintainable, and testable code following enterprise-grade engineering best practices.
• Problem-Solving and Ownership: Strong analytical skills and the ability to work independently in a fast-paced environment while translating business needs into technical solutions.