Senior AI Engineer

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.

• →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.

• 3–5+ years professional backend engineering experience in Python, FastAPI or Flask, and background processing.
• Masters Degree in related field
• 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

• 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.