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Gramian Consulting Group

Forward Deployed AI Engineer - Multiple Teams

AI Application Engineering +1Confirmed live in the last 24 hours
Full-timeNo salary listed

About the Job

Requirements

  • At least 6 years of professional software engineering experience, with strong production-level Python expertise.
  • Hands-on experience building LLM-powered applications in production.
  • Strong experience with LangChain, LangGraph, LlamaIndex, or comparable AI orchestration frameworks.
  • Strong understanding of Retrieval-Augmented Generation (RAG), vector retrieval, embeddings, and LLM integration patterns.
  • Experience designing AI agents, agentic workflows, tool/function calling, structured outputs, and automation systems.
  • Experience with React, Next.js, or similar modern web technologies.
  • Experience deploying production applications using cloud platforms, Docker, and CI/CD.
  • Strong understanding of enterprise integrations, secure API design, IAM, and AI/LLM-specific security considerations.
  • Ability to translate business needs into technical architecture and communicate effectively with technical and non-technical stakeholders.
  • Fluent English.

Responsibilities

  • Work directly with business, product, and engineering stakeholders to understand complex requirements and translate them into production-ready AI solutions.
  • Design and build applications using LLMs, RAG, AI agents, tool/function calling, and structured outputs.
  • Develop agentic workflows using frameworks such as LangChain, LangGraph, LlamaIndex, or similar technologies.
  • Design retrieval architectures using vector search, embeddings, document pipelines, and contextual retrieval techniques.
  • Build robust backend services and application logic in Python.
  • Develop user-facing AI applications using React, Next.js, or similar frontend technologies.
  • Integrate AI applications with enterprise APIs, internal platforms, data systems, and authentication/IAM environments.
  • Deploy applications using cloud platforms, Docker, and CI/CD pipelines.
  • Monitor and improve AI application quality, reliability, latency, security, and business performance.
  • Contribute reusable engineering patterns, components, and implementation best practices across projects.

About the Company

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