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Neander

Backend + AI Engineer

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Description

We are looking for an AI Engineer with strong backend engineering skills to build and deploy AI-powered products and applications. This role will be 80% focused on AI/GenAI development and 20% on backend engineering. The ideal candidate should have hands-on experience building LLM-based applications, AI agents, RAG pipelines, and AI workflows, along with strong experience in developing scalable backend systems and APIs.


The core responsibilities for the job include the following:


AI Engineering - 80%:

  • Design, develop, and deploy AI/GenAI-powered applications.
  • Build and optimize LLM-based applications, RAG pipelines, AI agents, and agentic workflows.
  • Work with frameworks such as LangChain, LangGraph, LlamaIndex, or similar.
  • Integrate LLMs and AI models from providers such as OpenAI, Anthropic, or open-source models.
  • Work on prompt engineering, model evaluation, context management, and improving AI application performance.
  • Develop AI solutions that can be deployed and used in production environments.
  • Collaborate with product and engineering teams to convert business problems into AI-powered solutions.


Backend Engineering - 20%:

  • Design and develop scalable backend services and APIs.
  • Build and maintain RESTful APIs and backend integrations.
  • Work with databases, cloud services, and distributed systems.
  • Ensure scalability, reliability, security, and performance of backend applications.
  • Integrate AI services and models with existing backend systems.


Requirements:

  • 4-8 years of overall software engineering experience.
  • Strong programming experience in Python.
  • Hands-on experience in building production-level AI/GenAI applications.
  • Strong understanding of LLMs, RAG, embeddings, vector databases, and AI agents.
  • Experience with LangChain, LangGraph, LlamaIndex, or similar frameworks.
  • Experience integrating APIs from OpenAI, Anthropic, or open-source models.
  • Good understanding of backend development, APIs, databases, and system design.
  • Experience with cloud platforms such as AWS, GCP, or Azure.
  • Ability to build scalable and production-ready AI applications.

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