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.