AI Engineer – Enterprise

Job not on LinkedIn

🔥 15 hours ago

🏄 California – Remote

🏄 California – Remote

⏰ Full Time

🟡 Mid-level

🟠 Senior

🤖 AI Engineer

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HR POD - Hiring Talent Globally

WebsiteLinkedInAll Job Openings

11 - 50 employees

Founded 2023

👥 HR Tech

🎯 Recruiter

🤝 B2B

HR Tech • Recruitment • B2B

HR POD is a leading premium global recruitment agency dedicated to elevating human resource management services. With a focus on helping companies strategize and build robust HR frameworks, HR POD specializes in recruitment, training, and performance management. They pride themselves on serving a diverse client base, particularly in the tech industry, and adopt a data-driven approach to optimize HR practices that align with organizational goals. Their commitment to integrity, customer satisfaction, and excellence positions them as a reliable partner in enhancing talent acquisition and retention strategies for businesses worldwide.

📋 Description

• 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.

🎯 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.