### About Us We’re a fast-growing product company integrating cutting-edge AI capabilities into our core offering to stay competitive and deliver exceptional value to customers. Our AI work spans task-specific ML models, large language model (LLM) integration, and agentic systems that orchestrate multiple tools to produce end-user results. We run a Python-based backend (FastAPI + Gunicorn + Nginx) with heavy background job processing using Celery. We’re looking for a senior-level AI Engineer who is equally strong in backend engineering and applied AI — capable of building production-grade systems that are fast, reliable, and maintainable. ### What You’ll Do - Design, develop, and deploy production-grade AI-powered backend systems. - Integrate LLMs and traditional ML models into performant, scalable architectures. - Integrate and optimize vector databases for retrieval-augmented generation (RAG) pipelines and other traditional ML queries. - Write clean, well-structured, and testable Python code following best practices. - Capable of thinking about performance and ensuring optimal decision making to reduce latency. - Build hybrid architectures that balance LLM calls with traditional ML. - Debug complex, cross-layer issues spanning backend, AI inference, and UI integration. - Conduct thorough dev testing before QA handoff to ensure production reliability. - Collaborate with product, backend, and frontend engineers to deliver cohesive solutions. ### Must-Have Skills & Experience - 3–5+ years professional backend engineering experience in Python, FastAPI or Flask, and background processing. - Proven record of deploying Python applications to production (not just scripts or academic work). - Strong grasp of software design patterns - Strong understanding of backend performance, parallel processing in background jobs and multi-threading - Proficiency in performance tuning specially for heavy AI models - Applied machine learning experience — training, evaluating, and maintaining small task-specific models. - Familiarity with LLM integration, prompt engineering, and context window optimization. - Proven ability to debug AI behavior, identify root causes, and make targeted fixes. - Strong testing discipline for both backend and AI components. - Experience with background processing with Celery or other major libraries - Experience with monitoring APIs and background processing - Experience with ensuring visibility and error reporting. - Nice to have: experience with Docker, understanding of CI/D, deployment automation and Kubernetes ### Who Will Succeed in This Role - Independent problem solver — you can debug without constant supervision. - Production mindset — you understand that reliability, scalability, and maintainability matter as much as accuracy. - System thinker — you see backend, AI, and UI as a connected whole. ### Why Join Us - Direct impact on the company’s competitive edge. - Small, fast-moving team with high autonomy. - Work on practical, real-world AI applications — not just research. - Opportunity to shape our AI architecture and best practices from the ground up. If you’re a backend-first AI engineer who thrives in shipping production-ready systems and knows how to make AI practical, fast, and reliable — we’d love to talk.