AI Engineer

Nitor Infotech
United States
1 day ago
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Role details

Contract type
Permanent contract
Employment type
Full-time (> 32 hours)
Experience level
Expert
Experience required
8 years minimum
Working hours
Regular working hours
Job source

Tech stack

Artificial Intelligence Amazon Web Services Software Applications Automated Storage and Retrieval Systems Microsoft Azure Distributed Systems Python (Programming Language) Node.Js Open Source Technology Software Engineering TypeScript Google Cloud
+7 more
Large Language Models Generative AI Containerization Low Latency Graphql Docker Microservices

Job description

  • Design and develop AI-powered applications using TypeScript, Node.js, and Python.
  • Integrate LLMs such as OpenAI, Anthropic, and open-source models into production systems.
  • Build AI agents, RAG pipelines, and intelligent automation workflows.
  • Develop scalable REST/GraphQL APIs and microservices.
  • Work with embeddings, vector databases, and knowledge retrieval systems.
  • Optimize AI applications for performance, scalability, latency, and cost.
  • Collaborate with product, engineering, and data teams to deliver AI-driven solutions.
  • Mentor junior engineers and contribute to architecture and technical decisions.

Requirements

We are seeking a Senior AI Engineer with strong expertise in TypeScript, Node.js, and Python to design, build, and deploy scalable, production-grade AI applications.

The ideal candidate will have hands-on experience with LLMs, Generative AI, RAG, AI agents, vector databases, and intelligent automation, along with strong software engineering and system design skills., * 8+ years of software engineering experience.

  • Strong TypeScript, Node.js, and Python skills.
  • Hands-on LLM / Generative AI experience.
  • Experience with RAG and AI agents.
  • Experience with LangChain, LlamaIndex, or Semantic Kernel.
  • Experience with vector databases such as Pinecone, Weaviate, Chroma, or FAISS.
  • Strong REST/GraphQL, microservices, and distributed systems experience.
  • Experience with AWS, Google Cloud Platform, or Azure.
  • Experience with Docker and containerized deployments.
  • Strong system design, problem-solving, and communication skills.

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