Edge AI/Model Optimization Engineer

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 compute platforms when operational demands exceed X9 Spider capabilities or when cost, performance, power, size, weight, or thermal tradeoffs require additional analysis
• Benchmark agentic AI workflows, inference pipelines, and model-serving architectures against target hardware constraints and operational performance thresholds
• Recommend model-selection, runtime, and configuration tradeoffs balancing mission effectiveness, latency, throughput, resource utilization, reliability, and operational sustainability
• Build and maintain repeatable performance and stress-testing frameworks for evaluating latency, throughput, tool-call overhead, failover behavior, degraded-resource conditions, and disconnected operational scenarios on edge compute platforms
• Package, deploy, validate, and sustain local model-serving components and inference services to support reliable operation within tactical and edge environments
• Collaborate with agent engineers, AI developers, and integration teams to validate that agent behavior, workflow reliability, and operational outcomes remain acceptable following model compression, quantization, runtime optimization, or hardware configuration changes
• Support deployment, troubleshooting, optimization, and sustainment activities for AI-enabled applications operating in edge, airborne, tactical, or disconnected operational environments
• Train customer technical personnel on supported model profiles, operational constraints, runtime tuning considerations, deployment limitations, troubleshooting procedures, and platform sustainment best practices
• Maintain technical documentation, benchmarking results, model validation reports, deployment procedures, optimization baselines, configuration guides, and operational support materials
• Support DevSecOps and CI/CD activities associated with AI model packaging, deployment automation, runtime validation, and operational release processes

Requirements

• 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
• Familiarity 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
• Strong analytical, troubleshooting, and performance optimization skills
• Ability to communicate technical findings and operational tradeoffs effectively to technical and non-technical stakeholders

Qualifications

• Active Security Clearance is required

Desired Qualifications

• 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

About NextGen

NextGen Federal Systems is an innovative technology and professional services provider specializing in advanced software solutions and comprehensive mission and business support services. We work in close collaboration with our customers to truly understand their business and mission goals. Our approach is to design, build, implement, and manage solutions that measurably improve our client’s organizational performance. We have established and foster a corporate culture where we:

• Treat employees with fairness and respect regardless of their position, sexual identity, race, or tenure
• Communicate the importance of our mission and our employees’ contributions to it, ensuring they understand how their job role contributes to the greater good
• Openly promote and communicate our ideas for change and adaptability
• Strive to achieve results as an organization
• Hold employees accountable to their commitments and provide incentives that encourage positive and productive behaviors
• Create an environment where people can engage at all levels
• Encourage people to take risks and allow them to make mistakes
• Value the talents and contributions of our employees as the key factor for our success