NextGen Federal Systems
technology and professional services
EdgeAI/ModelOptimizationEngineer
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“Edge AI/Model Optimization Engineer at NextGen Federal Systems. Skills: Edge AI, Model Optimization, LLMs, GPU Computing. Evaluate candidate LLMs, embedding models, AI inference solutions. Tune and optimize AI model runtime configurations”
What You'll Achieve.
ensure AI-enabled capabilities remain performant, reliable, and mission-effective; balancing mission effectiveness, latency, throughput, resource utilization, reliability, and operational sustainability; validate that agent behavior, workflow reliability, and operational outcomes remain acceptable
Industry & Context.
analytical, troubleshooting, and performance optimization skills.
Active Security Clearance is required
What They're Looking For.
Must Have
Bachelor’s degree in Computer Science, Electrical Engineering, Computer Engineering, Data Science, Artificial Intelligence, or related technical discipline., 5+ years of experience supporting AI/ML deployment, model optimization, edge computing, GPU acceleration, or AI inference operations., Experience deploying and optimizing LLMs, embedding models, or AI inference pipelines within resource-constrained or edge-compute environments., Experience with GPU-enabled systems and inference optimization technologies such as CUDA, TensorRT, ONNX Runtime, vLLM, Ollama, or equivalent platforms., Experience tuning AI runtime configurations including quantization, batching, caching, and memory optimization techniques., Experience benchmarking AI models and operational workflows against hardware performance constraints., Experience with Linux-based systems, containerized deployments, and orchestration technologies such as Docker and Kubernetes., Familiarity with Python and AI/ML deployment frameworks commonly used for edge inference and operational AI systems., analytical, troubleshooting, and performance optimization skills., Ability to communicate technical findings and operational tradeoffs effectively to technical and non-technical stakeholders., Active Security Clearance is required
Nice to Have
Experience supporting tactical, airborne, or mission-command edge computing environments., Familiarity with X9 Spider Mission Computer architectures or similar embedded GPU-enabled mission systems., Experience supporting AI-enabled workflows within NGC2, AIDP, EMSCO, Lattice, or related operational ecosystems., Experience with model quantization techniques such as INT8, FP16, GGUF, GPTQ, AWQ, or similar optimization approaches., Familiarity with disconnected, degraded, intermittent, and low-bandwidth (DDIL) operational environments., Experience with hardware evaluation and performance trade studies for operational edge compute systems.
What You'll Do.
Evaluate candidate LLMs
AI inference solutions
Tune and optimize AI model runtime configurations
Collaborate with customer stakeholders
Benchmark agentic AI workflows
Recommend model-selection
configuration tradeoffs
Build and maintain repeatable performance testing frameworks
sustain local model-serving components
Collaborate with agent engineers
sustainment activities
Train customer technical personnel
Maintain technical documentation
Support DevSecOps and CI/CD activities
How You'll Work.
Team & Collaboration
close collaboration with AI engineers, systems integrators, mission stakeholders, and operational users; Collaborate with customer stakeholders; Collaborate with agent engineers, AI developers, and integration teams
Communication Scope
Ability to communicate technical findings and operational tradeoffs effectively to technical and non-technical stakeholders.
Full Job Description
## Description NextGen is seeking a highly motivated and technically skilled Edge AI/Model Optimization Engineer to support the deployment, optimization, and sustainment of AI and agentic AI capabilities within edge and tactical computing environments. This role focuses on evaluating, tuning, benchmarking, and operationalizing Large Language Models (LLMs), embedding models, and AI inference services for constrained hardware platforms, including the X9 Spider Mission Computer architecture and other edge compute systems supporting operational missions using ReadiChat. ReadiChat is a mission-focused, agentic AI platform designed to help organizations build, deploy, govern, and scale specialized AI agents for operational workflows. It combines AI agents, workflow orchestration, grounded knowledge, testing frameworks, and enterprise controls into a single collaborative workspace. The ideal candidate will possess expertise in AI model optimization, GPU-enabled edge computing, runtime performance tuning, and operational AI deployment. This role requires close collaboration with AI engineers, systems integrators, mission stakeholders, and operational users to ensure AI-enabled capabilities remain performant, reliable, and mission-effective within disconnected, degraded, intermittent, and low-bandwidth environments. ## Responsibilities Evaluate candidate Large Language Models (LLMs), embedding models, and AI inference solutions for quality, latency, memory utilization, reliability, and operational performance on embedded GPU-enabled edge compute platforms, including the X9 Spider Mission Computer architecture. Tune and optimize AI model runtime configurations for edge deployment, including quantization strategies, batching configurations, context window sizing, cache behavior, inference scheduling, and GPU memory utilization specific to operational edge hardware environments. Collaborate with customer stakeholders to assess mission requirements and evaluate alternative edge
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