Senior AI Engineer

NovoEd, Inc.

About The Position

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.

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 Haves

  • Experience with Docker
  • Understanding of CI/CD
  • Deployment automation
  • Kubernetes

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.
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