About the Role
Pragmatike is recruiting on behalf of a European deep-tech company building AI-native cloud services and distributed AI infrastructure. Their platform delivers managed inference, LLM-as-a-Service, enterprise RAG solutions, and custom B2B model deployments, supporting real-world production workloads across text, image, and multimodal AI systems.
We are seeking an AI Engineer to join a highly technical AI Services team building production-grade GenAI and AI infrastructure products. This role is focused on model optimization, inference performance, AI system design, and enterprise AI deployments, working at the intersection of software engineering, machine learning, and cloud-native infrastructure.
You will play a key role in building scalable AI services that power real customer workloads, with strong ownership, technical autonomy, and direct impact on production systems.
What Youll Do
- Optimize model inference using advanced techniques including quantization (GPTQ, AWQ, GGUF), distillation, pruning, and speculative decoding
- Build and integrate GenAI capabilities beyond LLMs, including computer vision, image generation (Stable Diffusion, FLUX), and multimodal models
- Design and implement pre-processing and post-processing pipelines, including prompt engineering, structured output parsing, guardrails, and context management
- Build RAG systems, embedding pipelines, and semantic retrieval architectures for enterprise AI applications
- Drive model selection, benchmarking, and cost/performance trade-off decisions across AI services
- Build evaluation frameworks to measure model quality, latency, reliability, and production performance
- Build production AI systems that go beyond experimentation and notebooks, focusing on scalability, reliability, and maintainability
- Collaborate closely with platform, infrastructure, and product teams to deliver integrated AI services
- Contribute to AI platform architecture and long-term technical direction
- Participate in the full lifecycle of AI systems, from research and prototyping to production deployment and operations
Why Join Us
Our client is redefining cloud infrastructure through decentralization and advanced automation, offering a sovereign, energy-efficient alternative to hyperscale cloud providers. Youll join a deeply technical environment where architecture matters, performance is critical, and your decisions will directly shape the evolution of a complex, ambitious platform operating at the intersection of distributed systems, networking, and cloud infrastructure.
Why This Role Will Pivot Your Career
- Fully remote work from anywhere (EMEA timezone preferred)
- Equipment budget to build your ideal technical workspace
- Company offsites to connect with a highly technical international team
- Career growth within a scaling engineering and AI organization
- Work on cutting-edge distributed systems, AI infrastructure, and production GenAI platforms
What Were Looking For
- 3+ years of software engineering experience with at least 1+ year focused on AI/ML systems
- Hands-on experience with model optimization techniques including quantization, distillation, and fine-tuning
- Strong Python skills and experience with modern ML frameworks (PyTorch, Transformers, diffusers)
- Solid understanding of modern LLM architectures, inference patterns, and GenAI ecosystems
- Experience building real production AI applications (not just research prototypes or notebooks)
- Strong engineering mindset with focus on reliability, scalability, and maintainability
- Ability to move fast while maintaining production-grade quality standards
- Ownership mentality and comfort operating in early-stage, fast-moving environments
Bonus Points
- Experience with computer vision, image/video generation, or multimodal AI systems
- Background in embedding models, vector databases, and semantic retrieval at scale
- Familiarity with structured generation, function calling, agent frameworks, or orchestration systems
- Experience with distributed systems, cloud-native platforms, or AI infrastructure
- Exposure to cost-optimization strategies for large-scale AI inference systems
