Geospatial Platform Engineer

Vantor Services•McLean, VA

3d•Onsite

About The Position

Vantor is seeking a Geospatial Platform Engineer to support geospatial, imagery, AI/ML, and data-driven application development, deployment, and operations. This role will focus on moving mission capabilities from prototype to production through hands-on software development, data and service integration, Kubernetes-based platforms, containerized architectures, CI/CD automation, observability, and secure DevSecOps practices. The ideal candidate is a self-directed engineer with demonstrated experience developing and supporting geospatial, imagery, AI/ML, or analytic applications in secure hybrid environments, including cloud, on-premises, virtualized, and classified infrastructure. This person should be comfortable researching unfamiliar technologies, implementing solutions with minimal guidance, and collaborating across software, AI/ML, cloud, security, and mission teams. To be eligible for this position, you must be a U.S. Citizen. This position requires an active U.S. Government security clearance, applicants who do not currently hold the required clearance will not be eligible for consideration. Employment for cleared roles is contingent upon verification of clearance status. Certain roles may be subject to U.S. export control laws, requiring U.S. person status as defined by 8 U.S.C. 1324b(a)(3). This position requires an active U.S. Government Security Clearance at the TS/SCI level with required polygraph.

Requirements

Bachelor’s degree in computer science, information systems, engineering, data science, geospatial science, or related area of study.
Minimum 8 years of experience with a Bachelor’s degree; or 7 years of experience with a Master’s degree; or 6 years of experience with a Doctorate.
Active/current TS/SCI with required polygraph; US citizenship required; willingness to work onsite full time.
Strong experience developing production software using languages such as Python, Java, C++, JavaScript, TypeScript, SQL, Bash, or similar languages.
Demonstrated experience supporting geospatial, imagery, AI/ML, data-driven, or analytic applications.
Experience with Docker, Kubernetes, CI/CD pipelines, infrastructure automation, and containerized architectures in hybrid, on-premises, virtualized, or classified environments.
Ability to work independently, research and evaluate technical approaches, implement solutions with minimal guidance, document decisions, and collaborate across technical teams.

Nice To Haves

Demonstrated experience supporting geospatial analytics, imagery systems, mapping platforms, earth observation, full-motion video, EO/SAR, remote sensing, or related mission applications.
Experience developing geospatial services, data connectors, backend APIs, plugin architectures, image processing utilities, or analytic platform components.
Familiarity with 3D geospatial visualization, Cesium, OMAR image library, terrain/tileset services, imagery catalogs, or map-based analytic applications.
Experience with computer vision, image/video processing, object detection, tracking, classification, segmentation, feature extraction, or model inference workflows.
Hands-on experience with Apache Airflow, PostgreSQL/PostGIS, Elasticsearch, Trino/Presto, and other geospatial technologies.
Experience supporting model integration, ML pipelines, scalable inference services, NVIDIA Triton, data fusion, tipping and alerting, or production AI/ML runtime environments.
Experience with Rancher, RKE2, OpenShift, AWS, Azure, VMware vSphere, HashiCorp Vault, Artifactory, GitOps, Prometheus, Grafana, ELK/OpenSearch, CIS benchmarks, STIGs, ATO processes, or related tooling and practices.

Responsibilities

Develop and integrate backend services, APIs, data connectors, plugins, and platform components that support geospatial, imagery, AI/ML, and analytic workflows.
Build data ingest, enrichment, transformation, indexing, alerting, API, and analytic service components for mission-focused applications.
Design, deploy, and maintain containerized application architectures using Docker, Kubernetes, Rancher, RKE2, or similar technologies.
Create and improve CI/CD pipelines, reusable deployment patterns, infrastructure automation, and DevSecOps workflows using tools such as Ansible, Terraform, Packer, or similar technologies.
Support geospatial visualization, 3D terrain/model visualization, imagery access, and map-based analytic workflows using tools and frameworks such as Cesium, OMAR, or similar technologies.
Operationalize AI/ML models at scale through model integration, inference services, deployment automation, monitoring, and production support.
Troubleshoot complex issues across application code, Linux, containers, Kubernetes, CI/CD pipelines, APIs, networking, data services, and AI/ML-enabled environments.