Edge AI/Model Optimization Engineer

Job 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 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. ### Required Qualifications - 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. - Strong 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 ### 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.