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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # AI/ML Engineer (Computer Vision) - **Company:** AQUA IT - **Location:** Springfield, VA, United States - **Contract:** Permanent contract - **Skills:** Training Data, Artificial Intelligence, Amazon Web Services, Amazon Elastic Compute Cloud, Amazon S3, Computer Vision, Continuous Integration, Data Cleansing, Distributed Computing Environment, Machine Learning, Language Modeling, Pytorch, Large Language Models, Deep Learning, Question Answering, ONNX (Open Neural Network Exchange) Format, HuggingFace, Data Management, Machine Learning Operations, TensorRT, Software Version Control, Data Pipelines, Docker, Data Generation - **Published:** June 3, 2026 - **Apply:** https://www.indeed.com/viewjob?jk=ecd1ce52b74ea434 ## About the Role Do you have experience in Version control systems?, * TS/SCI with CI Poly required * 5+ years of professional machine learning engineering experience with a focus on deep learning * 1+ years of hands-on experience fine-tuning large foundation models (LLMs or VLMs) * Experience with parameter-efficient fine-tuning methods (LoRA, QLoRA, adapters) * Familiarity with supervised fine-tuning, instruction tuning, and RLHF/DPO alignment techniques * 4+ years of advanced Python development for ML workloads * Strong proficiency with PyTorch and the HuggingFace ecosystem (Transformers, PEFT, Datasets, Accelerate) * Experience with distributed training frameworks (DeepSpeed, FSDP, or Megatron) * 3+ years of experience with computer vision or multimodal models * Understanding of vision transformer architectures (ViT, CLIP, LLaVA-family models, or similar) * Experience processing and augmenting image datasets at scale * 3+ years of experience with AWS ML infrastructure SageMaker Training jobs, Processing jobs, and endpoint deployment GPU instance selection, multi-node training, and cost optimization on EC2 (P4/P5/G5/G6e), S3 data management for large-scale training datasets * 2+ years of experience building ML evaluation pipelines Automated benchmarking, metric computation, and result analysis * Experience with both quantitative metrics and qualitative/human evaluation approaches * Strong software engineering fundamentals (version control, testing, CI/CD for ML workflows), * 2+ years of experience with geospatial or remote sensing imagery * Familiarity with electro-optical and SAR satellite imagery formats and characteristics * Understanding of geospatial metadata, coordinate systems, and imagery preprocessing * Experience with model quantization and inference optimization (vLLM, TensorRT, ONNX) * Experience with MLOps and experiment tracking tools (MLflow, Weights & Biases, SageMaker Experiments) * Familiarity with data annotation platforms and active learning workflows for imagery * Experience with containerized ML workflows (Docker, ECR, ECS/EKS) * 2+ years of experience with Authority to Operate (ATO) processes in government environments * Implementation of NIST 800-53 controls and security compliance for ML systems * Experience deploying models in air-gapped or disconnected environments * Familiarity with multimodal evaluation benchmarks (MMMU, MMBench, GQA, or domain-specific equivalents) * Publications or demonstrated contributions in computer vision, VLMs, or multimodal AI * Experience with synthetic data generation for training data augmentation ## Description * Design and execute fine-tuning pipelines for Vision-Language Models (VLMs) on domain-specific imagery datasets, including data preprocessing, training orchestration, and hyperparameter optimization * Develop and implement evaluation frameworks for multimodal model performance, including task-specific metrics for image understanding, visual question answering, and spatial reasoning * Build scalable training infrastructure on AWS (SageMaker, EC2 GPU instances) for distributed fine-tuning of large multimodal models * Engineer data pipelines for curating, annotating, and transforming geospatial imagery datasets into model-ready formats for supervised and instruction-tuning workflows * Collaborate with applied scientists and solutions architects to iterate on model architectures, adapter strategies (LoRA/QLoRA), and inference optimization techniques ## Related Videos - [Tour de Force: Open-Source LLM Inference Optimization from Simple to Sophisticated](https://www.wearedevelopers.com/videos/100099-tour-de-force-open-source-llm-inference-optimization-from-simple-to-sophisticated) - [Machine Learning for Software Developers (and Knitters)](https://www.wearedevelopers.com/videos/154-machine-learning-for-software-developers-and-knitters) - [Photonic Computing: Programming a New Class of AI Accelerators (incl. Live Coding)](https://www.wearedevelopers.com/videos/100196-photonic-computing-programming-a-new-class-of-ai-accelerators-incl-live-coding) - [Docker Compose: Rediscovered](https://www.wearedevelopers.com/videos/1978-docker-compose-rediscovered) - [Efficient deployment and inference of GPU-accelerated LLMs​](https://www.wearedevelopers.com/videos/929-efficient-deployment-and-inference-of-gpu-accelerated-llms) - [DevOps for AI: running LLMs in production with Kubernetes and KubeFlow](https://www.wearedevelopers.com/videos/1222-devops-for-ai-running-llms-in-production-with-kubernetes-and-kubeflow) ## Related Articles - [Got AI ideas but no money? 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