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Design and deploy production ML/AI architectures on the Databricks platform, lead Generative AI (LLM, RAG, agent) implementations, optimize and scale MLOps pipelines, provide technical pre-sales support, and translate customer feedback into product improvements while mentoring peers and meeting delivery milestones.

Advise enterprise customers on designing, securing, administering, and deploying Databricks environments on Microsoft Azure. Architect scalable cloud infrastructure, networking, identity, disaster recovery, observability, and infrastructure-as-code solutions. Support pre-sales activities through proof-of-concept architectures and technical content, while sharing expertise through training, tutorials, hackathons, conferences, and open-source contributions.

Customer-facing technical leader who architects, builds, and optimizes large-scale data engineering and lakehouse solutions on Databricks. Supports pre-sales and delivery with POCs, performance/load testing, workload sizing, migrations, observability, and adoption through workshops and community engagement.

Own the vision, strategy, and roadmap for Databricks’ AI-first learning platform. Build contextual guidance, adaptive learning paths, learner models, assessments, skills graphs, analytics, and personalization tools. Partner with engineering, UX, curriculum, education, and go-to-market teams to deliver scalable learning experiences for customers, partners, and employees. Define metrics, understand skills gaps, engage users, and improve platform adoption through embedded learning capabilities.

Guide enterprise customers in designing, securing, administering, and deploying Databricks environments on AWS. Architect scalable infrastructure, networking, identity, and security solutions; support pre-sales engagements through proofs of concept and technical content; and provide technical leadership, training, tutorials, and community contributions. The role requires expertise in AWS, distributed data architectures, infrastructure automation, programming, SQL, and customer-facing technical consulting, with travel up to 30%.

Provides technical leadership and pre-sales guidance for Databricks Customer Lake implementations. Designs customer data, CDP, Lakehouse, identity resolution, audience activation, personalization, and agentic AI solutions; leads executive engagements, workshops, demos, POCs, and technical collateral. Partners with account teams, GSIs, and consulting organizations to drive adoption, influences product roadmaps, handles tier-three escalations, improves qualification processes, and mentors field teams.

Customer-facing engineer who designs, builds, and productionizes GenAI applications (RAG, multi-agent, Text2SQL, fine-tuning). Own production rollouts, advise customers, collaborate with product and engineering, present at conferences, and optimize/evaluate models and deployments across cloud platforms.

Leads global marketing planning, investment allocation, capacity modeling, and target setting. Builds and manages a new team while redesigning marketing workflows for AI and agent-based execution. Drives organization-wide adoption, establishes standards for prompts and context, and implements AI governance around quality, risk, privacy, and responsible use. Partners with Marketing, Finance, Sales Strategy, and data and platform teams to improve pipeline efficiency, time to market, and growth across business units.

Leads platform incident investigations, coordinates detection and resolution, performs root cause analyses, and designs customer-focused monitoring, alerting, and observability workflows. Builds automation, improves platform reliability, mentors engineers, and participates in on-call rotations. The role requires cloud, container orchestration, monitoring, logging, alerting, incident management, and production automation expertise.

Leads Databricks’ AI transformation strategy with enterprise C-suite executives and strategic partners. Builds trusted-advisor relationships, co-creates AI transformation roadmaps, and oversees execution, deployment, operating models, and value realization. Develops repeatable executive engagement and co-sell programs, leads C-suite workshops, and establishes an external AI Transformation Expert Advisory Council. Requires extensive enterprise digital and AI transformation leadership, C-suite engagement, industry expertise, operating-model development, systems-integrator collaboration, and sufficient technical credibility.