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

United States·San Mateomid

Machine Learning EngineerData

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Quick Summary

Key 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,

Requirements Summary

4–8 years of experience in AI Engineering, Applied AI, Machine Learning Engineering, Infrastructure Engineering, Field Engineering, Solutions Architecture, or a similar technical role.

Technical Tools

Machine Learning EngineerData

Requirements

~2 min read

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

~1 min read

→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.
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