Forward Deployed AI Engineer
New
G
Gramian ConsultancyAI software
Remote — Turkey, Croatia, Czech Republic, Estonia, Georgia, Poland, or RomaniaContractSenior
Salary not disclosed
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Job Details
- Languages
- Fluent English is required.
- Experience
- 6+ years of professional software engineering experience
- Required Skills
- DockerPythonNext.jsReactLangChain
Requirements
- Have 6+ years of professional software engineering experience.
- Bring strong production-level Python expertise.
- Have hands-on experience building LLM-powered applications in production.
- Have experience with LangChain, LangGraph, LlamaIndex, or comparable AI orchestration frameworks.
- Understand RAG, vector retrieval, embeddings, and LLM integration patterns.
- Have experience designing AI agents, agentic workflows, tool and function calling, structured outputs, and automation systems.
- Have experience with React, Next.js, or similar modern web technologies.
- Have experience deploying production applications using cloud platforms, Docker, and CI/CD.
- Understand enterprise integrations, secure API design, IAM, and AI- and LLM-specific security considerations.
- Be able to translate business needs into technical architecture and communicate with technical and non-technical stakeholders.
- Be fluent in English.
Responsibilities
- Work with business, product, and engineering stakeholders to understand requirements and translate them into production-ready AI solutions.
- Design and build applications using LLMs, RAG, AI agents, tool and function calling, and structured outputs.
- Develop agentic workflows with LangChain, LangGraph, LlamaIndex, or similar technologies.
- Design retrieval architectures using vector search, embeddings, document pipelines, and contextual retrieval techniques.
- Build 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 and 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.
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