AI Engineer

Job Description

As a Staff AI Engineer, you will serve as a technical leader and force multiplier within the AI Engineering Enablement team. You will define how AI systems are architected, deployed, and scaled across the organization. Operating at the intersection of deep technical expertise and organizational leadership, you will shape platform strategy, influence roadmaps, and elevate engineering capabilities across multiple teams.

Grade:
Technical 412

Pay Range: $ - $

• Define and own AI engineering architecture standards, design patterns, and platform conventions for LLM‑based systems
• Lead complex, cross‑functional AI initiatives from inception through delivery, aligning engineering, product, data science, and research stakeholders
• Drive build‑vs‑buy and vendor evaluation decisions for AI frameworks, models, and infrastructure
• Design and scale internal AI platforms including shared tooling, reusable components, prompt libraries, and evaluation infrastructure
• Establish and mature LLMOps practices including governance, cost management, observability, and safe deployment standards
• Lead AI safety initiatives including red‑teaming, adversarial testing, and responsible AI policy development
• Mentor and develop Senior and II‑level engineers through coaching, design reviews, and technical leadership

• Expert‑level, production‑proven experience across the AI engineering stack including LLM APIs, agentic systems, RAG pipelines, evaluation frameworks, and LLMOps
• Demonstrated ability to define and drive architectural patterns and engineering standards at team or organizational scale
• Deep expertise in agentic system design including multi‑agent architectures, state management, and reliability engineering for non‑deterministic systems
• Strong platform engineering experience designing shared infrastructure, reusable tooling, and developer‑facing systems
• Advanced knowledge of LLM fine‑tuning, alignment techniques, and evaluation methodologies including safety and bias assessment
• Experience leading vendor evaluations and technical due diligence for AI frameworks and infrastructure
• Strong proficiency in Python and software engineering fundamentals with a focus on quality, testing, and reliability standards
• Bachelor’s Degree in Computer Science, Software Engineering, Data Science, Machine Learning, Math, or a related field. Master’s Degree strongly preferred.
• 7+ years of experience in software engineering, data science, or machine learning
• 5+ years of hands‑on experience building and deploying LLM‑based or AI systems in production at scale
• Demonstrated experience setting architectural direction across teams or organizations
• Experience leading complex AI projects across multiple teams or functional areas
• Proven mentorship of Senior and/or II‑level engineers
• Experience designing and operating shared AI platforms or internal AI infrastructure
• Experience owning LLMOps or MLOps practices including governance, rollout strategy, and production monitoring
• Experience with AWS cloud architectures including scalable inference, data pipelines, and cost optimization
• Hands‑on experience with fine‑tuning, PEFT, and model evaluation in production environments
• Experience with enterprise AI governance,

• Master’s Degree or PhD in Computer Science, AI/ML, or a related field
• Experience with Databricks and related certifications
• AWS certifications such as AWS Certified Machine Learning – Specialty
• Experience with open‑source model ecosystems and self‑hosted inference infrastructure
• Experience in EdTech, personalized learning, or student‑facing AI platforms
• Published research, conference presentations, or open‑source contributions in AI/ML