NovoEd
Senior AI Engineer
Edtech.com's Summary
- Design and deploy AI-powered backend systems using Python and FastAPI.
- Integrate large language models (LLMs), traditional ML models, and vector databases for RAG pipelines.
- Write clean, testable Python code with strong performance and reliability focus.
- Debug and optimize AI inference, backend, and UI integration issues.
- Collaborate closely with product, backend, and frontend engineers.
- 3–5+ years of professional backend engineering with Python, FastAPI or Flask, and background processing.
- Experience in performance tuning of AI models and applied machine learning with task-specific models.
- Proficient in Celery for background job processing and monitoring APIs.
- Familiarity with LLM integration, prompt engineering, and debugging AI behavior.
- Nice to have experience with Docker, CI/CD, deployment automation, and Kubernetes.
Senior AI Engineer Full Description
About Us
Weâre 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. Weâre looking for a senior-level AI Engineer who is equally strong in backend engineering and applied AI â capable of building production-grade systems that are fast, reliable, and maintainable.
What Youâll 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
- 3â5+ 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 â 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 â you can debug without constant supervision.
- Production mindset â you understand that reliability, scalability, and maintainability matter as much as accuracy.
- System thinker â you see backend, AI, and UI as a connected whole.
Why Join Us
- Direct impact on the companyâs competitive edge.
- Small, fast-moving team with high autonomy.
- Work on practical, real-world AI applications â not just research.
- Opportunity to shape our AI architecture and best practices from the ground up.
If youâre a backend-first AI engineer who thrives in shipping production-ready systems and knows how to make AI practical, fast, and reliable â weâd love to talk.
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