About the position We are looking for a remote Mid-Level AI Engineer with hands-on experience building and deploying Generative AI solutions. 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.

Responsibilities

• Implement retrieval-augmented generation techniques to customize LLMs for practical business use.
• Use AWS Bedrock, OpenAI or similar APIs to embed generative AI into existing systems.
• Build AI-powered tools to enhance operational efficiency, decision-support systems, or customer workflows in industry settings.
• Work with data engineering to preprocess and manage data for model inputs, ensuring security and compliance.
• Monitor and refine LLM deployments in scalable, reliable environments.
• Collaborate with software engineers, product managers, and stakeholders to deliver AI solutions that meet real needs.
• Create clear documentation and explain technical concepts to both technical and non-technical audiences in a hands-on context.

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.
• 1–3 years (or more) of applied experience in Software, Data, or ML Engineering (e.g., backend, data pipelines, model implementation).
• Strong Python skills and experience with ML frameworks (TensorFlow, PyTorch, scikit-learn).
• Familiarity with AWS, GCP, or Azure integration.
• Interest in LLMs, prompt engineering, RAG, and applied AI, demonstrated through project work or prior deployments.
• Ability to take a project from prototype to delivery, optimizing for performance and business value.
• Clear communication, cross-functional collaboration, agile mindset. Nice-to-haves
• 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.