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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Artificial Intelligence / Machine Learning Data Engineer - **Company:** MAG LLC - **Location:** Fairfax, VA, United States - **Experience:** Expert - **Contract:** Permanent contract - **Skills:** Artificial Intelligence, Airflow, Computer Vision, Encodings, Nvidia CUDA, Custom Software, Information Engineering, Extract Transform Load (ETL), Graph Database, Hardware Design, Python (Programming Language), Machine Learning, NoSQL, Object Detection, OpenCV, Open Source Technology, Power BI, Software Tools, Tensorflow, Sensor Fusion, Smart Devices, SQL Databases, Data Streaming, Tableau (Software), Video Editing, Feature Engineering, Pytorch, Large Language Models, Grafana, Apache Spark, Kubernetes, ONNX (Open Neural Network Exchange) Format, Hardware Acceleration, Machine Learning Operations, TensorRT, Virtual Agents, Data Pipelines - **Published:** July 23, 2026 - **Apply:** https://dejobs.org/x/x/49BEA34BB7FB49EB929F9E6B17E3675A/job/ ## About the Role * 5+ years' experience in machine learning, AI, and data engineering * Strong proficiency in Python and ML frameworks (PyTorch, TensorFlow, JAX) * Experience with modern AI paradigms (transformers, diffusion models, neural ODEs) * Hands-on experience with LLM deployment and optimization (vLLM, TGI, llama.cpp) * Proficiency with data engineering tools (Apache Spark, Airflow, dbt, etc.) * Experience with both SQL and NoSQL databases at scale * Knowledge of vector databases and embedding systems (Pinecone, Weaviate, pgvector) * Experience with computer vision libraries (OpenCV, PIL) and video processing * Understanding of MLOps practices and model lifecycle management, * Experience with military/defense AI applications * Knowledge of agentic AI frameworks (LangChain, AutoGPT, CrewAI) * Familiarity with federated learning and edge-cloud hybrid architectures * Experience with business intelligence tools (Tableau, PowerBI, Grafana) * Knowledge of time-series analysis and anomaly detection * Experience with knowledge graphs and semantic reasoning * Understanding of explainable AI and model interpretability * Experience with MLOps platforms and tools (e.g., MLflow, Kubeflow, Weights & Biases) * Published research or patents in relevant areas Education & Experience: * Bachelor's degree in CS, EE, or related field; * Master's preferred Clearance: * Must be eligible for Secret security clearance ## Description MAG Aerospace is staffing for a Artificial Intelligence / Machine Learning Data Engineer. This position will lead the development of intelligent systems that transform multi-modal sensor data into actionable intelligence for tactical operations. You'll leverage COTS, FOSS/OSS, and custom development to build or integrate everything from edge computer vision to conversational AI assistants, while managing the data pipelines that feed these systems in the most challenging environments. While you'll have a core expertise in either data engineering or model development, you have a passion for mastering the full stack of AI systems. US Citizens Only Former US Defense Contractor / US Gov / US Military Experience Only This is a Hybrid Position - Remote mainly - but as well on call to come into a MAG office when requested We are seeking candidates who live in proximity to our corporate HQ in Fairfax, VA primarily but will entertain persons living near our satellite offices in, Duties include, but not limited to, * Develop and optimize data-centric AI solutions such as computer vision pipelines for object detection, tracking, and classification * Implement advanced AI capabilities including RAG systems, agentic workflows, and fine-tuned LLMs * Design and deploy edge-optimized models using TensorRT, ONNX, and quantization techniques * Build data engineering pipelines for ETL, feature engineering, and model training * Create analytics dashboards and business intelligence solutions for operational insights * Implement multi-modal sensor fusion algorithms (visual, thermal, acoustic, RF) * Design and maintain data lakes, warehouses, and real-time streaming architectures * Develop conversational AI interfaces using open-source LLMs (Llama, Mistral, etc.) * Establish and enforce data quality standards, validation checks, and governance procedures throughout the data lifecycle * Develop and implement robust testing and validation strategies for AI/ML models, including performance under degraded data conditions, adversarial testing, and operational scenarios Secondary Responsibilities: * Optimize AI workloads for embedded platforms (Jetson, Intel Neural Compute Stick) * Implement hardware acceleration using CUDA and TensorRT * Profile and optimize memory/power consumption for edge devices * Support embedded systems team with AI-specific hardware integration * Design distributed inference systems for degraded network conditions ## Related Videos - [Introduction to Azure Machine Learning](https://www.wearedevelopers.com/videos/368-introduction-to-azure-machine-learning) - [From Messy Queries to Scalable Systems - How Data Engineering actually works](https://www.wearedevelopers.com/videos/100203-from-messy-queries-to-scalable-systems-how-data-engineering-actually-works) - [Deepfakes in Realtime - How Neural Networks Are Changing Our World](https://www.wearedevelopers.com/videos/180-deepfakes-in-realtime-how-neural-networks-are-changing-our-world) - [Leveraging Real time data in FSIs](https://www.wearedevelopers.com/videos/806-leveraging-real-time-data-in-fsis) - [AI Model Management Life Circles: ML Ops For Generative AI Models From Research to Deployment](https://www.wearedevelopers.com/videos/1152-ai-model-management-life-circles-ml-ops-for-generative-ai-models-from-research-to-deployment) - [Coffee with Developers - Maria Apazoglou](https://www.wearedevelopers.com/videos/1209-coffee-with-developers-maria-apazoglou) ## Related Articles - [How to Become an AI Engineer](https://www.wearedevelopers.com/magazine/331-how-to-become-an-ai-engineer) - [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) - [What Are Large Language Models?](https://www.wearedevelopers.com/magazine/304-what-are-large-language-models) - [Graph and AI Trends 2026: Why Is AI Running but Not Yet Delivering?](https://www.wearedevelopers.com/magazine/680-graph-and-ai-trends-2026-why-is-ai-running-but-not-yet-delivering) - [Coffee with Developers - Maria Apazoglou - Making AI understandable for all in production](https://www.wearedevelopers.com/magazine/475-coffee-with-developers-maria-apazoglou-making-ai-understandable-for-all-in-production)