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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Cloud DevSecOps Engineer - SOCEUR - **Company:** CACI - **Location:** Stuttgart, Germany - **Salary:** €115,600.0 - **Contract:** Permanent contract - **Skills:** Artificial Intelligence, Amazon Web Services, Amazon Elastic Compute Cloud, Amazon S3, Audio Signal Processing, BigQuery, C++ (Programming Language), Cloud Computing, Cloud Storage, CompTIA Security+, Nvidia CUDA, Continuous Integration, Data Flow Control, Python (Programming Language), Machine Learning, Natural Language Processing, NumPy, Object Detection, OpenCV, Tensorflow, Azure Machine Learning, Search Technologies, Software Deployment, Reinforcement Learning, Jupyter Notebook, Graphics Processing Unit (GPU), Google Cloud, Pytorch, Large Language Models, Multi-Agent Systems, Prompt Engineering, Multi-Cloud, Generative AI, Keras, Gitlab, Git, Pandas, Core Data, Scikit Learn, Kubernetes, Information Technology, Low Latency, HuggingFace, Rancher, Bitbucket, Machine Learning Operations, Virtual Agents, Functional Programming, GPT, Software Version Control, Devsecops, Docker - **Published:** July 19, 2026 - **Apply:** https://www.adzuna.de/details/5805423292 ## About the Role Required: * Must be a US Citizen possessing an active TS/SCI security clearance. * Bachelor's degree in computer science, Artificial Intelligence, Data Science, or a related field. * 8+ years of experience in full-stack software development (Python or C++) with a focus on designing and testing production-grade software. * 5+ years of dedicated experience in ML engineering, model deployment, and ML infrastructure optimization. * Hands-on expertise utilizing GCP data and ML services, specifically Vertex AI, BigQuery, Dataflow, and Cloud Storage. * 2+ years of experience with Generative AI technologies, including LLMs, RAG, embedding models, and vector databases (e.g., Vertex AI Vector Search). * 1+ years of experience building and scaling Agentic workflows/prompt engineering. * Proficiency with core data science and ML libraries (e.g., PyTorch, TensorFlow, Keras, scikit-learn, Pandas, NumPy, OpenCV). * Experience contributing to enterprise codebases using version control (Git, GitLab, Bitbucket) and CI/CD pipelines. * DoD 8570.01-M / DoD 8140 IAT Level II (or higher) baseline certification (e.g., CompTIA Security+, CISSP). * Google Cloud Professional Machine Learning Engineer certification (or equivalent GCP Professional certification). * . Desired: * TensorFlow Developer Certificate. * Familiarity with Agentic and LLM frameworks such as LangChain, HuggingFace, Model Context Protocol (MCP), or Microsoft Agent Framework. * Experience utilizing hardware-acceleration frameworks, specifically compiling and optimizing code for Google TPUs (utilizing JAX and XLA) as well as NVIDIA GPUs (utilizing CUDA, CuPy, Numba, or the RAPIDS/CuDF ecosystem). * Experience in application deployment and container orchestration (Docker, Podman, Kubernetes, Rancher). * Familiarity with multi-cloud environments, including AWS resources (EC2, S3, Lambda). * Deep understanding of NLP algorithms (e.g., BERT) and specialized ML fields such as Speech/Audio processing or Reinforcement Learning ## Description As a CACI AI/ML Engineer to develop, deploy, and maintain custom artificial intelligence and machine learning models. This role sits inside the Special Operations Command Europe (SOCEUR) Hybrid Threat Action Platform (HTAP) program and is responsible for the end-to-end lifecycle of ML models on the Google Cloud Platform (GCP). The individual will build robust MLOps infrastructure, integrate vector databases, and optimize model inference to transition prototypes from isolated research environments into highly available production systems. The individual will lead the development of Agentic AI and large language model (LLM) capabilities across multiple defense disciplines including object detection, natural language processing (NLP), and time-series prediction. The individual will interact regularly with stakeholders ranging from technical leads at NASIC/DOE, HTAP engineers, to mission owners supporting Allied, partner, and US operations., * Architect, build, and deploy machine learning and Generative AI models utilizing Kubernetes, GCP's Vertex AI, and advanced infrastructure like Tensor Processing Units (TPUs). * Design retrieval-augmented generation (RAG) frameworks, manage multi-agent workflows, and fine-tune LLMs to solve complex, multi-layered defense and operational problems. * Transition ML prototypes from isolated Jupyter notebooks to highly available, low-latency, and secure scaled production environments. * Design and deploy model endpoints featuring strict security checks, error handling, retries, and fallback mechanisms. * Establish end-to-end continuous integration and continuous deployment (CI/CD) pipelines to ensure real-time inference and seamless model updates in tactical to strategic cloud environments. * Implement evaluation frameworks and guardrails to eliminate logical errors, hallucinations, and biases in automated operational decision-making. * Implement rigorous model governance, ensuring version control, auditability, and Explainable AI (XAI) standards are met. * Optimize infrastructure for high-performance computing (HPC) workloads and Edge AI deployments. * Act as a technical advisor to HTAP mission owners and stakeholders, driving AI/ML alignment, technical project strategy, and coaching teams on cloud ML integration. ## Related Videos - [Machine learning in the browser with TensorFlowjs](https://www.wearedevelopers.com/videos/155-machine-learning-in-the-browser-with-tensorflowjs) - [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) - [ Evaluating AI models for code comprehension](https://www.wearedevelopers.com/videos/1462-evaluating-ai-models-for-code-comprehension) - [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) - [Overview of Machine Learning in Python](https://www.wearedevelopers.com/videos/840-overview-of-machine-learning-in-python) - [Git for Code Reviews](https://www.wearedevelopers.com/videos/429-git-for-code-reviews) ## Related Articles - [Got AI ideas but no money? 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