AI Engineer Enterprise

Requirements:

• 48 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.