Senior AI/ML Developer – Backend Systems

Headquartered in Berlin, were a fast-growing B2B security SaaS platform making identity security smarter and simpler for enterprises worldwide. Youll be part of a diverse team that thrives on creativity, collaboration, and cross-border problem-solving. With cybersecurity now mission-critical, youll be building not just a career, but a future in one of techs most dynamic and resilient sectors. Joining Unosecur means stepping onto a global stage.

Role Overview

We are looking for a highly skilled AI/ML Developer with strong Backend Development expertise to design, build, and deploy production-grade AI-powered applications. The ideal candidate will have hands-on experience in Machine Learning, Generative AI, LLM-based systems, and scalable backend services, with the ability to translate research and prototypes into reliable, enterprise-ready solutions.

This role involves working across the full software development lifecyclefrom system design and model development to deployment, optimization, and monitoringwhile collaborating closely with product, platform, and DevOps teams.

Key Responsibilities

AI / Machine Learning

• Design, develop, and deploy end-to-end AI/ML applications, including traditional ML and Generative AI solutions.
• Building and integrating (MCP) within AI/ML systems
• Build and optimize LLM-based systems such as RAG pipelines, Agentic AI workflows, and domain-specific chatbots.
• Fine-tune and optimize models using techniques like PEFT, SFT, and prompt engineering (Few-Shot, Chain-of-Thought).
• Develop NLP solutions for text classification, sentiment analysis, summarization, and question answering.
• Ensure model performance, scalability, and reliability in production environments.

Backend Development

• Design and develop scalable backend services and REST APIs using Python (FastAPI/Flask).
• Integrate ML models into backend systems for real-time and batch inference.
• Build microservices-based architectures with strong emphasis on performance, security, and maintainability.
• Work with relational and NoSQL databases to support data-intensive AI applications.

MLOps & Deployment

• Implement MLOps workflows for experiment tracking, model versioning, and reproducible deployments.
• Containerize and deploy applications using Docker and CI/CD pipelines.
• Optimize inference pipelines for low latency and high throughput.
• Monitor deployed models and services, ensuring reliability and continuous improvement.

Collaboration & Ownership

• Collaborate with cross-functional teams including AI research, backend engineering, DevOps, and product.
• Participate in system design discussions and contribute to architectural decisions.
• Take ownership of features from concept to production, ensuring high-quality delivery.

Required Skills & Qualifications

Technical Skills

• Strong proficiency in Python with backend frameworks such as FastAPI or Flask.
• Solid experience in Machine Learning and Deep Learning frameworks (PyTorch, TensorFlow, scikit-learn).
• Hands-on experience with Generative AI, LLMs, RAG architectures, and Agentic AI systems.
• Experience building and consuming RESTful APIs and microservices.
• Knowledge of SQL and NoSQL databases (PostgreSQL, MongoDB, Redis, etc.).
• Experience with Docker, CI/CD pipelines, and cloud platforms (AWS, Azure, or equivalent).
• Familiarity with ML lifecycle management tools (experiment tracking, model registry, monitoring).

Software Engineering

• Strong understanding of system design, SDLC, and clean code practices.
• Experience with version control systems (Git) and testing frameworks.
• Ability to design scalable, maintainable, and production-ready systems.

Nice to Have

• Experience with Agentic AI frameworks and autonomous workflows.
• Exposure to LLM inference optimization and serving frameworks.
• Prior experience working in enterprise or research-driven environments.
• Contributions to patents, publications, or open-source projects.

What Were Looking For

• A problem-solver who can bridge AI research and backend engineering.
• Someone comfortable owning end-to-end delivery of AI-powered systems.
• Strong communication skills and the ability to work in fast-paced, collaborative environments.