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

Gramian Consulting Group · Remote · US, Czech Republic, Croatia +1 only

US, Czech Republic, Croatia +1 only6+ years requiredPublished 31 October 2026Looks fresh

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First recorded by Psst: 3 November 2026. Sign in with Google to continue to the employer. A free account is enough.

The 20-second read

Experience
6+ years required
Where you can live
US, Czech Republic, Croatia +1 only
Languages
English
Skills and tools
python, react, docker, ci/cd
Job type
Contract or freelance

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Job details

Where you can work
United States, Czech Republic, Croatia, Turkey
Employment type
Contract
Advertised pay
Not stated by the employer
Experience
6+ years required
Employer publication date
31 October 2026
First recorded by Psst
3 November 2026
Last checked at the source
3 November 2026

About the job

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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