Applied AI Architect

Databricks•United States, AZ

2h•$180,000 - $247,500•Remote

About The Position

As an Applied AI Architect, you will lead advanced AI/ML technical strategy for customers, own complex architecture discussions, drive platform adoption, and serve as a trusted advisor to customer technical leads and architects. You will combine deep technical expertise with strategic thinking to position Databricks as the foundation of customers’ data and AI strategy, while developing a technical specialization and becoming recognized within the Field Engineering team for your domain. This position can be remote. You will own the end-to-end AI/ML technical strategy for your accounts, from discovery through production deployment and consumption growth. You will design scalable, production-grade solutions across AI/ML, including RAG, tool calling, multi-agent orchestration, guardrails, AI evaluation, and observability systems. You will advise customer architects, engineering leads, and Directors; demonstrate Databricks’ differentiation in competitive scenarios; coordinate internal and partner resources; and provide structured customer feedback to influence product direction. The role requires substantial experience in solutions architecture, technical pre-sales, or a senior hands-on technical role; expertise in ML engineering, AI engineering, distributed data systems, and public cloud deployments; and the ability to code in Python and SQL and lead architecture discussions with senior technical stakeholders. Travel may be required up to 30% of the time. The stated local pay range is $180,000–$247,500 USD. The total compensation package may also include eligibility for an annual performance bonus, equity, and benefits.

Requirements

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

Nice To Haves

Databricks certifications in Data Engineer, ML, or Platform.
Experience with competitive platforms such as Snowflake, AWS native services, or Azure Synapse.
Background in a data/AI company or cloud provider.
Industry domain expertise in areas such as Financial Services, Healthcare, Retail, or Media.

Responsibilities

Own the end-to-end AI/ML technical strategy for accounts, from discovery through production deployment and consumption growth.
Lead complex architecture discussions and design scalable, production-grade AI/ML solutions, including RAG, tool calling, multi-agent orchestration, guardrails, AI evaluation, and observability systems.
Serve as a trusted technical advisor to customer architects, engineering leads, and Directors.
Drive technical wins in competitive scenarios by demonstrating Databricks’ differentiation through custom-built solutions.
Develop and declare an emerging technical specialization and become a go-to resource for the team in that domain.
Orchestrate cross-functional resources, including DSAs, SAs, and partners, to deliver comprehensive solutions for complex customer needs.
Provide structured feedback on customer requirements and competitive gaps to influence product direction.