> Markdown version of [/jobs/ext/1309085-telecommute-forward-deployment-engineer](https://www.wearedevelopers.com/jobs/ext/1309085-telecommute-forward-deployment-engineer). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # TELECOMMUTE Forward Deployment Engineer - **Company:** The Eeo - **Location:** United States (Remote available) - **Experience:** Expert - **Salary:** $138,000.0 - $170,000.0 - **Contract:** Permanent contract - **Skills:** Artificial Intelligence, Amazon Web Services, Microsoft Azure, C++ (Programming Language), Cloud Computing, Nvidia CUDA, Software Debugging, Python (Programming Language), Software Engineering, Google Cloud, Large Language Models, Multi-Agent Systems, Prompt Engineering, Generative AI, Gpu Programming, Containerization, Kubernetes, Information Technology, Low Latency, Deployment Automation, Machine Learning Operations, Docker - **Published:** July 17, 2026 - **Apply:** https://www.dice.com/job-detail/2f747310-4ff5-4e81-8c7c-3f4c7fedb5ee ## About the Role * 5+ years of hands-on engineering experience, with a strong record of shipping production AI/ML systems. * Deep expertise in GenAI application development: LLM orchestration, RAG, agentic frameworks (LangChain, LlamaIndex, DSPy), prompt engineering, and evaluation pipelines. * Strong foundations in ML fundamentals - model training, fine-tuning, inference optimization, quantization, and performance benchmarking. * Proficiency in Python (required); working knowledge of C++ or CUDA a strong plus for hardware-layer debugging. * Experience deploying AI workloads on cloud infrastructure (AWS, Azure, Google Cloud Platform) and familiarity with containerization, orchestration (Kubernetes, Docker), and MLOps tooling. * Comfortable engaging directly with customers: able to run technical discovery, set expectations, push back constructively, and present to executive and practitioner audiences alike. * Bachelor's or graduate degree in Computer Science, Electrical Engineering, Mathematics, Physics, or equivalent practical experience. * Willingness to travel up to 50% to customer sites - flexible based on engagement needs. Bonus qualifications: * Experience with AI accelerators or custom silicon (TPUs etc.) * CUDA / low-level GPU programming * Familiarity with VLLM / SGLang * Enterprise AI deployments in regulated industries ## Description * Embed directly with strategic enterprise customers to design, build, and deploy production GenAI applications on SambaNova's SN40L platform and SambaStack based product portfolio * Architect and implement LLM-powered workflows - including RAG pipelines, multi-agent systems, fine-tuning workflows, and coding solutions - tailored to each customer's data, infrastructure, and business goals. * Optimize AI inference performance on SambaNova hardware; benchmark model throughput, latency, and accuracy against customer requirements and competitor baselines. * Troubleshoot and resolve production issues end-to-end across model, software, and hardware layers - acting as the first and last line of technical escalation in the field. * Translate customer needs into clear product requirements and engineering feedback; serve as the primary voice of field reality to SambaNova's Product and Engineering teams. * Partner with Account Executives and Solutions Engineers to shape technical sales strategy, scope engagements, and demonstrate platform differentiation during evaluations and proof-of-concepts. * Develop reusable accelerators, reference architectures, and internal playbooks that scale learnings from one deployment to many. * Present technical findings, architecture decisions, and roadmap input at customer executive briefings and internal forums; represent SambaNova at industry conferences and events. ## Related Videos - [Docker Compose: Rediscovered](https://www.wearedevelopers.com/videos/1978-docker-compose-rediscovered) - [Understanding Kubernetes in a visual way](https://www.wearedevelopers.com/videos/100085-understanding-kubernetes-in-a-visual-way) - [Swapping Low Latency Data Storage Under High Load](https://www.wearedevelopers.com/videos/746-swapping-low-latency-data-storage-under-high-load) - [Shipping Faster with Less: Render on Cloud Hosting, AI Workloads, and the Future of DevOps](https://www.wearedevelopers.com/videos/1894-shipping-faster-with-less-render-on-cloud-hosting-ai-workloads-and-the-future-of-devops) - [Microservices: how to get started with Spring Boot and Kubernetes](https://www.wearedevelopers.com/videos/242-microservices-how-to-get-started-with-spring-boot-and-kubernetes) - [Docker build without Docker](https://www.wearedevelopers.com/videos/100114-docker-build-without-docker) ## Related Articles - [How to Become an AI Engineer](https://www.wearedevelopers.com/magazine/331-how-to-become-an-ai-engineer) - [Got AI ideas but no money? Here are 10 free ways to level up your AI skills with Google Cloud](https://www.wearedevelopers.com/magazine/600-got-ai-ideas-but-no-money-here-are-10-free-ways-to-level-up-your-ai-skills-with-google-cloud) - [MLOps And AI Driven Development](https://www.wearedevelopers.com/magazine/82-mlops-and-ai-driven-development) - [What Are Large Language Models?](https://www.wearedevelopers.com/magazine/304-what-are-large-language-models) - [Stephan Gillich - Bringing AI Everywhere](https://www.wearedevelopers.com/magazine/489-stephan-gillich-bringing-ai-everywhere) - [Navigating the AI Shift](https://www.wearedevelopers.com/magazine/629-navigating-the-ai-shift)