Forward Deployed AI Engineer (Python, LLMs, RAG) Geo Turkey, Croatia, Czech Rep

Gramian Consulting Group · posted Oct 31, 2026

Open to candidates in Croatia, Czechia and 5 more

Croatia, Czechia, Estonia, Georgia, Poland, Romania, Türkiye

Contract · Senior

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What this job asks for

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Must have

  • •6+ years professional software engineering experience
  • •Production-level Python expertise
  • •Hands-on LLM-powered application development
  • •Experience with RAG and LLM integration
  • •Experience with AI orchestration frameworks
  • •Strong stakeholder communication skills

Nice to have

  • •Experience with agentic workflows
  • •Experience with modern web technologies
  • •Experience with cloud platforms and Docker

Tools and technologies

Python, LLMs, RAG, LangChain, LangGraph, LlamaIndex, React, Next.js, Docker, CI/CD

Languages required

en

Read the full posting

Gramian Consultancy is a boutique consultancy specializing in IT professional services and engineering talent solutions. With a strong background in software engineering and leadership, we help companies build high-performing teams by matching them with professionals who truly fit their needs.

Role Overview

We are looking for an experienced Forward Deployed AI Engineer to design and deliver production-grade AI applications directly with enterprise product and engineering teams.

This is a highly hands-on, client-facing role focused on turning complex business requirements into scalable solutions using Python, Large Language Models, RAG, agentic workflows, LangChain / LangGraph, and modern web technologies . You will own the full delivery lifecycle, from discovery and rapid prototyping through production deployment, adoption, and continuous optimization.

CONTRACT: Contractor, long-term engagement

COMMITMENT: Full-time

LOCATIONS: Remote — Turkey, Croatia, Czech Republic, Estonia, Georgia, Poland, or Romania

START: ASAP

DURATION: 6+ months, with possible extension based on performance

NOTES: Fluent English and strong stakeholder communication skills are required.

Key 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.

Requirements

  • 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.
  • Strong ability to translate business needs into technical architecture and communicate effectively with both technical and non-technical stakeholders.
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