AI Model Engineer

Everforth ECS is seeking an AI Model Engineer to work in a hybrid remote/onsite capacity, with minimum of 3 business days onsite at our Fairfax, VA corporate office and/or our Ashburn, VA customer site. Please note: This position is contingent upon contract award Everforth ECS is seeking an accomplished AI/ML Engineer to provide technical leadership and strategic guidance on the integration of advanced artificial intelligence and machine learning solutions to support mission-critical government and defense objectives. The ideal candidate is innovative with a track record of evaluating, experimenting with, and transitioning emerging technologies into operational use. This role requires exceptional communication skills, strong technical expertise, and experience collaborating across the Department of Homeland Security (DHS) and other government agencies. The AI Engineer will design, train, implement, and maintain end-to-end machine learning algorithms and their pipelines, automating deployment and monitoring processes while ensuring performance, observability, and security. This role contributes to building scalable infrastructure, real‑time dashboards, and automated pipelines that enable secure, compliant, and efficient AI operations aligned with mission and business goals. Key responsibilities include: Design, build, train, and fine tune computer vision models for object detection, tracking, and classification tasks. Utilize multimodal architectures for robust retrieval and data fusion. Coordinate the planning, development, and execution of cutting-edge research programs designed to experimentally validate novel AI/ML concepts. Evaluate emerging technologies from academia and industry for their potential impact on national security. Serve as a subject matter expert, providing technical leadership and strategic recommendations to government decision-makers on AI/ML technology, adoption, and implementation. Set the technical direction for advanced computer vision and AI capabilities supporting exploitation of remote sensing data, including EO and hyperspectral imagery. Conduct rapid feasibility studies and prototype implementations to evaluate emerging algorithms, model architectures, and data exploitation approaches. Use prototype-driven demonstrations and technical studies to shape applied research programs and support proposal development for new AI initiatives. Advance the application of vision-language and multimodal foundation models for analysis, retrieval, and reasoning over large-scale EO/IR and hyperspectral datasets. Work closely with mission partners to refine problem definitions, evaluate prototype systems, and ensure developed capabilities transition rapidly into operational environments. Prepare and deliver high-quality technical briefings, documentation, and presentations for both technical and non-technical audiences Utilizing data pipelines in Databricks, Apache Spark, and related ETL technologies (e.g., AWS Glue, Apache Airflow). Ensuring compliance with DHS security and accreditation standards, including STIGs and Impact Level controls. Providing architectural oversight on data ingestion, curation, and storage to produce reliable, high-quality datasets for AI/ML development. Supporting DevSecOps practices, CI/CD pipelines, and automation to streamline delivery. Salary Range: $125,000-$150,000 General Description of Benefits Must be a US Citizen with the ability to obtain and maintain a Public Trust determination Minimum Bachelor’s degree in Computer Science, Electrical Engineering, or a related technical field 6+ years of experience in software engineering, data engineering, and cloud architecture Proficiency with AI/ML frameworks (e.g., TensorFlow, PyTorch, YOLO, ONNX) Proficiency in Linux -- system administration, scripting in Bash, troubleshooting Strong foundation in AI/ML algorithms and ability to implement agentic workflow, and prompt engineering Experience in large language model (LLM) applications Strong understanding of model evaluation metrics (e.g., precision, recall, F1) and statistical drift detection methods Expertise in containerization and orchestration (Docker, Kubernetes, OpenShift) and CI/CD automation (GitHub Actions, Jenkins) Proficiency in building and managing ETL pipelines (e.g. AWS Glue, Apache Airflow) Strong communication skills with the ability to interface and collaborate with project managers, stakeholders, vendors, and technical staff