Applied AI Architect
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Job Description
Applied AI Architect leading AI/ML architecture and technical strategy for Databricks customers. Driving production deployments, platform adoption, and competitive technical wins.
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
- Work with Retrieval-Augmented Generation, 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 through custom-built solutions demonstrating Databricks differentiation
- Develop and declare an emerging technical specialization and become a go-to resource for the Field Engineering team
- Orchestrate DSAs, SAs, and partners to deliver comprehensive solutions
- Influence product direction through structured feedback on customer requirements and competitive gaps
Requirements:
- 6+ years in solutions architecture, technical pre-sales, or a senior hands-on technical role
- Experience in ML Engineering, including cloud infrastructure on AWS, Azure, or GCP for production ML applications and drift monitoring
- Experience in AI Engineering with LLMs and agentic systems, vector databases, fine-tuning, AI guardrails, and frameworks 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, and cloud-native data platforms
- Proficiency on the Databricks Platform or demonstrated ability to achieve proficiency rapidly
- Developing technical specialization in an area such as real-time/streaming, ML/AI, data governance, or migrations
- 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
- Track record of 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, or a quantitative discipline, or equivalent experience
- Willingness to travel up to 30%
- Databricks certifications, competitive platform experience, data/AI company or cloud provider background, and industry domain expertise are nice to have
Benefits:
- Annual performance bonus eligibility
- Equity eligibility
- Comprehensive benefits and perks (region-specific details provided by Databricks)
- Remote work
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