Develop and deploy production-ready generative AI applications using Python, LLMs, RAG, AWS Bedrock, and OpenAI APIs. Responsibilities include model customization, API integration, data preparation, performance tuning, production deployment, documentation, and cross-functional collaboration. The role focuses on applied engineering rather than research and requires experience integrating AI solutions into business applications.
We are looking for a remote Mid-Level AI Engineer with hands-on experience building and deploying Generative AI solutions.
📍 Remote within Europe.
This is an engineering role focused on developing production-ready applications using Large Language Models (LLMs), RAG, and cloud AI platforms such as AWS Bedrock or OpenAI APIs. This is not a research-focused position.
Before you apply, please ensure you meet the following minimum requirements:
- Professional experience developing applications in Python.
- Hands-on experience building or deploying LLM-based applications.
- Experience with RAG, OpenAI/AWS Bedrock APIs, or similar GenAI frameworks.
- Experience integrating AI solutions into production or business applications.
- Strong English communication skills.
Key Accountabilities
- Model Customization & RAG: Implement retrieval-augmented generation techniques to customize LLMs for practical business use.
- API & Platform Integration: Use AWS Bedrock, OpenAI or similar APIs to embed generative AI into existing systems.
- Applied Solution Development: Build AI-powered tools to enhance operational efficiency, decision-support systems, or customer workflows in industry settings.
- Data Prep & Collaboration: Work with data engineering to preprocess and manage data for model inputs, ensuring security and compliance.
- Performance Tuning & Production Deployment: Monitor and refine LLM deployments in scalable, reliable environments.
- Cross-Functional Partnership: Collaborate with software engineers, product managers, and stakeholders to deliver AI solutions that meet real needs.
- Documentation & Communication: Create clear documentation and explain technical concepts to both technical and non-technical audiences in a hands-on context.
Qualifications Minimum Qualifications
- Background: 1–3 years (or more) of applied experience in Software, Data, or ML Engineering (e.g., backend, data pipelines, model implementation).
- Technical Fluency: Strong Python skills and experience with ML frameworks (TensorFlow, PyTorch, scikit-learn).
- Cloud Experience: Familiarity with AWS, GCP, or Azure integration.
- Generative AI Passion: Interest in LLMs, prompt engineering, RAG, and applied AI, demonstrated through project work or prior deployments.
- Problem-Solving & Ownership: Ability to take a project from prototype to delivery, optimizing for performance and business value.
- Soft Skills: Clear communication, cross-functional collaboration, agile mindset.
- Education: Bachelor’s or Master’s in Computer Science, Engineering, Data Science, PhD not required.
Pluses (Nice to Have)
- Experience with LLM fine-tuning, prompt engineering, or LangChain/Agent frameworks.
- Familiarity with MLOps tools (e.g., MLflow, Docker, CI/CD pipelines).
- Industry-specific experience (e.g. maritime, logistics, finance) is a bonus, but we prioritize applied engineering experience over domain knowledge.
We are looking forward to your application.
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