AI Engineer - Enterprise (Remote, USA - San Mateo, CA)

Requirements: 4–8 years of experience in AI Engineering, Applied AI, Machine Learning Engineering, Infrastructure Engineering, Field Engineering, Solutions Architecture, or a similar technical role. 3+ years of experience in customer-facing AI/ML or infrastructure roles, with a proven track record of leading technical workstreams for enterprise customers. Strong Python development experience. Proven experience deploying production AI or machine learning systems in enterprise environments. Hands-on experience with Large Language Models (LLMs), open-model inference frameworks, and modern model-serving stacks. Experience supporting model training, evaluation, and fine-tuning workflows, including SFT, DPO, and RFT. Strong understanding of cloud platforms, including AWS, Azure, or GCP, with hands-on experience in Kubernetes and containerized environments. Experience working with GPUs, distributed systems, performance-critical infrastructure, and AI infrastructure products and platforms. Knowledge of Retrieval-Augmented Generation (RAG) architectures. Strong communication skills, with the ability to engage both technical and executive audiences. Ability to navigate ambiguity, solve complex technical challenges, and maintain a customer-centric mindset with strong business acumen. Demonstrated executive presence, with the ability to engage deeply with engineers while clearly communicating technical trade-offs to senior leadership. Experience working in customer-facing engineering, field engineering, or solutions architecture roles. Experience deploying enterprise AI solutions and taking AI solutions from proof-of-concept to production. Experience influencing product strategy through customer engagement. Experience working in a startup or high-growth technology company, with the ability to thrive in fast-paced environments where speed, sound judgment, and ownership are essential. Responsibilities: Lead technical discovery sessions with enterprise customers to understand business objectives, deployment requirements, and success criteria. Scope and execute proof-of-concepts, pilot programs, and production deployment initiatives. Conduct load testing and evaluations to validate model architectures and deployment configurations. Design and implement end-to-end AI solutions within complex enterprise environments. Build production-grade AI and machine learning systems that meet enterprise performance, security, and compliance requirements. Conduct model evaluations, benchmarking, and performance testing. Advise customers on model selection strategies and deployment architectures. Support fine-tuning methodologies, including Supervised Fine-Tuning (SFT), Direct Preference Optimization (DPO), and Reinforcement Fine-Tuning (RFT). Develop evaluation frameworks to measure model quality and business impact. Design scalable inference architectures that support enterprise workloads. Work with GPU infrastructure, containerized applications, Kubernetes, and cloud platforms. Collaborate with customer engineering teams to optimize system reliability, latency, scalability, and performance. Address infrastructure, security, and compliance challenges to ensure successful production deployments. Present technical recommendations to engineering teams and executive leadership. Build trusted relationships with customer stakeholders, identify champions, address objections, and drive successful deployments. Identify recurring customer pain points and provide actionable feedback to internal product and engineering teams. Influence product roadmap decisions through customer insights and field experience.