Senior AI Engineer
Posted 1hrs ago
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Job Description
Senior AI Engineer at NovoEd, Inc. building production-grade AI systems using Python and integrating LLMs and traditional ML models.
Responsibilities:
- 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.
Requirements:
- 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.
Benefits:
- 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.















