AI/ML Engineer - ELSYS - Colorado Springs, CO - (Open Rank)

site-logo•Colorado Springs, CO

14h•Onsite

About The Position

The Georgia Tech Research Institute (GTRI) is seeking an AI/ML Engineer to support the United States Space Force (USSF) Space Systems Command (SSC), specifically the Operational Test and Training Infrastructure (OTTI) / System Delta 81. This role focuses on 'physical AI' for space systems, integrating AI with high-fidelity, physics-based satellite simulations and live operational data. The engineer will help build and integrate digital twin-style environments that synchronize with real satellite telemetry and sensor data for testing, training, and evaluating AI capabilities in space operations. The position emphasizes the foundational data, interoperability, and simulation architectures needed to deploy and evaluate advanced AI and Large Language Model (LLM) capabilities in DoD space simulation and live environments. Key responsibilities include engineering data infrastructure, semantic ontologies, API abstraction layers, and data pipelines connecting digital twins, AI training/evaluation workflows, and live systems.

Requirements

Experience developing software in Python for data processing, backend services, or basic machine learning workflows
Experience working with at least one relational database (e.g., PostgreSQL, MySQL) including schema design and querying
Experience building and consuming web APIs (e.g., REST or GraphQL) in a production or research environment
Experience integrating, deploying, testing, and tuning large language models
Experience implementing data pipelines that perform ingestion, transformation, and preparation of data for analytics or ML (e.g., ETL/ELT workflows)
Experience with at least one common ML or numerical computing framework (e.g., PyTorch, TensorFlow, Scikit-learn, NumPy) in a coursework, research, or professional context
Experience using software version control (e.g., Git) in a collaborative environment
U.S. Citizenship required due to research contracts with the U.S. federal government
Must be able to obtain and maintain an active security clearance

Nice To Haves

Active TS/SCI Clearance
Experience working with GPU-accelerated simulation or digital twin environments for robotics, aerospace, or space systems (e.g., NVIDIA Omniverse, Isaac Sim, or similar tools)
Experience integrating or querying data from the Unified Data Library (UDL) or other large-scale Department of Defense (DoD) or Intelligence Community data repositories
Familiarity with Space Domain Awareness (SDA), space operations, or Operational Test and Training Infrastructure (OTTI) concepts
Experience designing or integrating data models and ontologies for complex, multi-source operational data (e.g., sensor data, telemetry, simulation outputs)
Experience utilizing Large Language Model (LLM) orchestration frameworks (e.g., LangChain, LlamaIndex) for Retrieval-Augmented Generation (RAG) or tool-using agents
Experience deploying applications or services within DoD DevSecOps platforms (e.g., Platform One) or government cloud environments (e.g., AWS GovCloud, Azure Government)
Familiarity with Semantic Web standards (e.g., RDF, OWL, SPARQL) for building and mapping robust data ontologies and knowledge graphs
Experience with containerization and orchestration technologies (e.g., Docker, Kubernetes) in secure or resource-constrained environments

Responsibilities

Design complex system architectures (e.g., high-performance computing clusters, networks, chipsets, GPUs) based on available hardware (e.g., embedded systems, cloud, on-premise, etc.)
Lead a team of engineers responsible for system deployment
Develop novel algorithms and methodologies
Engage with sponsors to understand and meet system requirements
Serve as the primary author on technical reports and proposals
Bridge the gap between high-fidelity space simulation environments, AI training workflows, and live systems (e.g., NVIDIA-based digital twin environments) to operationalize AI in widely interoperable test, training, and operational contexts
Architect and develop API abstraction layers to consolidate and streamline data access across disparate relational, graph, and simulation data services for downstream AI/ML applications
Design and implement semantic ontologies and data models to standardize complex military datasets, such as the Unified Data Library (UDL), and to enable seamless integration with AI and Large Language Model (LLM) capabilities and orchestration frameworks (e.g., Model Context Protocol)
Build, optimize, and maintain robust data pipelines that connect live space data sources, UDL datasets, and simulation environments, including ingestion, knowledge graph development, transformation, and vectorization to support Retrieval-Augmented Generation (RAG) and other AI inference workflows
Translate legacy data systems and unstructured data stores into interoperable, AI-ready formats that can both drive and be driven by physics-based simulations in support of OTTI initiatives
Develop and maintain comprehensive technical documentation for data schemas, API endpoints, simulation-AI integration patterns, and AI system interoperability frameworks
Evaluate and integrate emerging commercial and open-source simulation, AI, and data engineering technologies into secure DoD/USSF environments, in coordination with OTTI stakeholders and system architects