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.