Freelance Mid-Level AI Engineer

Umanova SA; Genf; Remote; CHF 66,000 - 113,000; Full time

Job summary

Umanova SA is seeking a remote Mid-Level AI Engineer to develop production‑ready generative AI solutions using LLMs, RAG, and cloud platforms. The role emphasizes practical deployment over research and collaborates with software engineers and product teams across Europe.

You will build and integrate AI tools, optimize models for real‑world use, and ensure security and compliance while delivering value to business workflows.

Qualifications

• 1-3 years of applied software, data, or ML engineering experience.
• Strong Python skills and ML framework experience (TensorFlow, PyTorch, scikit-learn).
• Familiarity with cloud integrations (AWS, GCP, Azure).
• Interest in LLMs, RAG, prompt engineering, and applied AI projects.
• Proven ability to ship production-grade AI solutions.

Responsibilities

• Model customization and RAG to tailor LLMs for business use.
• API & platform integration using AWS Bedrock, OpenAI, or similar APIs.
• Develop AI-powered tools to enhance operations and decision-making.
• Collaborate with data engineers to prep data for models and ensure security/compliance.
• Monitor and refine deployments for scalability and reliability.
• Document work and communicate technical concepts to diverse audiences.

Skills

Python

ML frameworks

Communication

Cross-functional collaboration

Education

Bachelor's or Master's in Computer Science, Engineering, Data Science

Tools

AWS Bedrock

OpenAI API

LangChain

Docker

CI/CD

Job description

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.

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.