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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Ai Platform Engineer / Ai Infrastructure Engineer - **Company:** Axiomatic AI - **Location:** Madrid, Spain (Remote available) - **Contract:** Permanent contract - **Skills:** Application Programming Interfaces (APIs), Artificial Intelligence, Amazon Web Services, Bash Shell, Cloud Computing, Encodings, Continuous Integration, Information Engineering, Software Debugging, DevOps, Distributed Systems, Electronic Design Automation, Hardware Design, Monitoring of Systems, Python (Programming Language), Machine Learning, Language Modeling, Performance Tuning, Software Engineering, SQLAlchemy, Data Streaming, Management of Software Versions, AI Infrastructure, Data Logging, Google Cloud, Large Language Models, Prompt Engineering, Backend, Git, Fastapi, AI Platforms, Kubernetes, HuggingFace, Machine Learning Operations, Api Design, Terraform, Docker - **Published:** August 5, 2026 - **Apply:** https://www.buscojobs.com.es/ai-platform-engineer-ai-infrastructure-engineer-en-madrid-ID-365578475 ## About the Role Required Skills & Experience 7+ years of software engineering experience (Python preferred). Experience with LLMs and AI/ML in production: Open AI API, Hugging Face, Lang Chain, or similar. Knowledge of vector databases: Pinecone, Chroma, Weaviate, FAISS. Cloud infrastructure experience: GCP (Vertex AI preferred) or AWS (Sage Maker). API development: Fast API, REST, async programming. CI/CD and Dev Ops: Docker, Terraform, Git Hub Actions. Monitoring and observability. Problem?solving mindset; comfortable debugging complex distributed systems. Experience deploying AI at enterprise level. Nice-to-Have Fine?tuning or training models. Familiarity with Lang Chain, Pydantic AI, or similar frameworks. Prompt engineering and evaluation techniques. Real?time inference and streaming responses. Background in data engineering or ML engineering. Knowledge of RAG architectures. Contributions to open?source AI/ML projects. Tech Stack Languages: Python, Bash. AI/ML: Open AI API, Anthropic, Hugging Face, Lang Chain, Pydantic AI. Vector DBs: Pinecone, Chroma, Weaviate, FAISS. Backend: Fast API, SQLAlchemy, Pydantic. Cloud: GCP (Vertex AI, Cloud Run), Terraform. CI/CD: Git Hub Actions. Experiment Tracking: MLflow, Weights & Biases, or custom. Containers: Docker; Kubernetes optional. ## Description Join to apply for the AI Platform Engineer / AI Infrastructure Engineer role at Axiomatic_AI About us Axiomatic_AI is dedicated to accelerating R&D by developing the next generation of Automated Interpretable Reasoning, a verifiably truthful AI model built for reasoning in science and engineering.We empower engineers in hardware design and Electronic Design Automation (EDA), with a mission to revolutionize the fields of hardware design and simulation in the photonics and semiconductor industry.Position Overview As an AI Platform Engineer, you will bridge AI research and production software, building and maintaining AI infrastructure, enabling developers to deploy work safely, and designing APIs for AI inference, prompt management, and evaluation.You will also implement MLOps pipelines, optimize performance, and collaborate closely with backend engineers.Key Responsibilities Build and maintain AI infrastructure: model serving, vector databases, embedding pipelines.Enable AI developers to deploy reproducibly and safely.Design APIs for AI inference, prompt management, and evaluation.Implement MLOps pipelines: versioning, monitoring, logging, experimentation tracking.Optimize performance: latency, cost, throughput, and reliability.Collaborate with backend engineers to integrate AI capabilities.Monitor model performance, drift, and set up logging and observability.Build CI/CD pipelines for model deployment.Document AI infrastructure and best practices.Mentor AI developers on software practices.Required Skills & Experience 7+ years of software engineering experience (Python preferred).Experience with LLMs and AI/ML in production: Open AI API, Hugging Face, Lang Chain, or similar.Knowledge of vector databases: Pinecone, Chroma, Weaviate, FAISS.Cloud infrastructure experience: GCP (Vertex AI preferred) or AWS (Sage Maker).API development: Fast API, REST, async programming.CI/CD and Dev Ops: Docker, Terraform, Git Hub Actions.Monitoring and observability.Problem?solving mindset; comfortable debugging complex distributed systems.Experience deploying AI at enterprise level.Nice-to-Have Fine?tuning or training models.Familiarity with Lang Chain, Pydantic AI, or similar frameworks.Prompt engineering and evaluation techniques.Real?time inference and streaming responses.Background in data engineering or ML engineering.Knowledge of RAG architectures.Contributions to open?source AI/ML projects.Tech Stack Languages: Python, Bash.AI/ML: Open AI API, Anthropic, Hugging Face, Lang Chain, Pydantic AI.Vector DBs: Pinecone, Chroma, Weaviate, FAISS.Backend: Fast API, SQLAlchemy, Pydantic.Cloud: GCP (Vertex AI, Cloud Run), Terraform.CI/CD: Git Hub Actions.Experiment Tracking: MLflow, Weights & Biases, or custom.Containers: Docker; Kubernetes optional.What We Offer Competitive compensation, including Stock Options.Access to state?of?the?art tools and collaboration with leading experts.Flexible work arrangements with potential remote options.Opportunities for professional growth: conferences, conferences, publications.A culture focused on impact in AI and hardware.Why Join Us At Axiomatic_AI we drive innovation in AI for scientific and engineering applications.Your contributions will help bring new AI architectures that reason coherently and produce verifiable solutions to market, shaping the future of hardware and computing.We celebrate diversity and encourage applicants who may not meet every qualification to apply; we are eager to consider a wide range of experiences and backgrounds.#J-*****-Ljbffr ## Related Videos - [How a Small Team Shrank a Microsoft Monorepo by 94%](https://www.wearedevelopers.com/videos/1236-how-a-small-team-shrank-a-microsoft-monorepo-by-94) - [Docker Compose: Rediscovered](https://www.wearedevelopers.com/videos/1978-docker-compose-rediscovered) - [From DevOps to Scaled DevOps: How We’re Rebuilding Continuous Delivery as a Platform](https://www.wearedevelopers.com/videos/100018-from-devops-to-scaled-devops-how-we-re-rebuilding-continuous-delivery-as-a-platform) - [Shipping Faster with Less: Render on Cloud Hosting, AI Workloads, and the Future of DevOps](https://www.wearedevelopers.com/videos/1894-shipping-faster-with-less-render-on-cloud-hosting-ai-workloads-and-the-future-of-devops) - [Git for Code Reviews](https://www.wearedevelopers.com/videos/429-git-for-code-reviews) - [Developer Experience, Platform Engineering and AI powered Apps](https://www.wearedevelopers.com/videos/990-developer-experience-platform-engineering-and-ai-powered-apps) ## Related Articles - [How to Become an AI Engineer](https://www.wearedevelopers.com/magazine/331-how-to-become-an-ai-engineer) - [Navigating the AI Shift](https://www.wearedevelopers.com/magazine/629-navigating-the-ai-shift) - [MLOps And AI Driven Development](https://www.wearedevelopers.com/magazine/82-mlops-and-ai-driven-development) - [MLOps – What’s the deal behind it?](https://www.wearedevelopers.com/magazine/125-mlops-what-s-the-deal-behind-it) - [Dev Digest 132 - Binging WADFlix?](https://www.wearedevelopers.com/magazine/473-dev-digest-132-binging-wadflix) - [What Are Large Language Models?](https://www.wearedevelopers.com/magazine/304-what-are-large-language-models)