Cloud DevSecOps Engineer - SOCEUR

Cloud Dev Sec Ops  Engineer - SOCEUR

The Opportunity:

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.

Responsibilities:

• 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.

Qualifications:

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, Big Query, 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, Tensor Flow, Keras, scikit-learn, Pandas, Num Py, OpenCV).
• Experience contributing to enterprise codebases using version control (Git, Git Lab, 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:

• Tensor Flow Developer Certificate.
• Familiarity with Agentic and LLM frameworks such as Lang Chain, Hugging Face, 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…