Job Description:
As a Senior AI Engineer – Geospatial Intelligence & Advanced AI, you will lead the design, implementation, and operationalization of production‑grade AI capabilities that turn multi‑source geospatial data into decision‑ready intelligence. You will work closely with Product Managers, Data Engineers, Software Developers, and domain experts to deliver scalable, reliable solutions across geospatial computer vision (e.g., satellite/remote‑sensing imagery), graph/topology‑aware modeling (networks and relational geospatial structures), optimization, and anomaly detection.
“Advanced AI” in this role is an accelerator: you will selectively apply modern AI patterns (including multimodal/foundation‑model capabilities where appropriate) to improve analyst workflows, automation, and interpretability—while keeping GEOINT accuracy, traceability, and operational reliability as the primary success criteria.
You will be hands‑on while also providing technical leadership—driving best practices for model development, ML system architecture, MLOps, reliability engineering, and responsible deployment.
Responsibilities- Lead end-to-end delivery of ML/AI solutions from problem framing and prototyping to production deployment, monitoring, and continuous improvement.
- Provide technical direction on model selection, experimentation strategy, evaluation methodology, and architecture.
- Mentor and review work of AI Engineers; raise engineering standards through code reviews, design reviews, and reusable patterns.
- Build AI‑powered geospatial intelligence workflows that transform imagery and geospatial data into actionable outputs (alerts, change maps, object inventories, situational overlays).
- Partner with domain teams to define geospatial product requirements, acceptance criteria, and quality thresholds; translate them into model and pipeline requirements.
- Drive dataset strategy for GEOINT tasks (labeling guidelines, sampling, balancing, provenance, and ground‑truth validation).
- Ensure model outputs are map‑ready: georeferenced, interpretable, and compatible with cartographic and downstream GEOINT production pipelines (e.g., layers, metadata, confidence measures, and audit trails).
- Design, train, tune, and validate Deep Learning and classical ML models for geospatial computer vision (e.g., object detection/segmentation/change detection on satellite imagery).
- Develop and evaluate Graph Neural Network (GNN) models and topological learning approaches for problems involving spatial relationships, networks, and infrastructure graphs.
- Apply graph‑based learning for use cases such as transport networks, connectivity analysis, supply chains, and geospatial relationship modeling.
- Contribute to analyst productivity and decision support by integrating advanced AI patterns where they measurably help (e.g., multimodal models for triage, structured extraction, search/summarization over GEOINT artifacts), with clear evaluation, governance, and guardrails.
- Define and implement evaluation approaches for advanced AI components (offline test sets, human‑in‑the‑loop review, red‑teaming where relevant) and ensure outputs are reliable, attributable, and policy‑compliant.
- Build robust feature engineering pipelines and evaluation frameworks, including task‑specific metrics (e.g., mAP/IoU, geospatial accuracy), graph metrics, and GenAI quality measures.
- Own and improve model performance, generalization, and bias/variance trade‑offs.
- Build and launch models in production using best practices in CI/CD, model versioning, experiment tracking, and automated testing.
- Define and manage SLAs/SLOs for…
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