Applied AI Architect

Quick Facts

• Remote option available
• Travel up to 30% as needed

Description

As an Applied AI Architect, you will own end-to-end AI/ML technical strategy for customer accounts—from discovery through production deployment and growth in AI consumption. You will lead complex architecture discussions and design scalable, production-grade AI/ML solutions including RAG, tool calling, multi-agent orchestration, guardrails, AI evaluation, and observability. You will serve as a trusted technical advisor to customer technical leaders while driving platform adoption and developing a specialized technical archetype.

Responsibilities

• Own AI/ML technical strategy end-to-end (discovery → production deployment → consumption growth)
• Lead complex architecture discussions for scalable, production-grade AI/ML solutions
• Advise customer architects, engineering leads, and directors
• Drive competitive technical wins with custom-built differentiation
• Develop and act as a go-to resource in an emerging technical specialization (archetype)
• Orchestrate cross-functional resources (DSAs, SAs, Partners)
• Provide structured customer feedback to influence product direction and identify competitive gaps

Benefits

• Annual performance bonus eligibility, equity, and comprehensive benefits (region-dependent)
• Diversity and inclusion commitment and equal employment opportunity compliance

Requirements

• 6+ years in solutions architecture, technical pre-sales, or a senior hands-on technical role
• Production ML engineering experience on AWS/Azure/GCP, including cloud infrastructure and drift monitoring
• Hands-on AI engineering with LLMs and agentic systems (vector databases, fine-tuning, AI guardrails) and frameworks/APIs such as LangChain, Hugging Face, or OpenAI APIs
• Strong coding proficiency in Python and SQL, including live coding, debugging, and solution building
• Deep expertise in distributed data systems architecture (scalable pipelines, streaming architectures, lakehouse patterns, cloud-native data platforms)
• Proficiency with the Databricks Platform (or ability to achieve proficiency rapidly) and developing technical specialization
• Proven ability to lead architecture discussions with senior technical stakeholders (whiteboarding, design reviews, trade-off analysis)
• Production deployment experience on public cloud including infrastructure, security, and governance considerations
• Track record driving platform adoption and consumption growth within accounts
• Excellent communication skills for translating complex architectures into business value
• Bachelor’s or Master’s degree in Computer Science/Engineering/quantitative discipline (or equivalent experience)
• Willingness to travel up to 30% as needed