Senior Databricks AI Engineer

The Senior AI/ML Engineer will modernize and scale the company’s enterprise data and AI platform by designing AI-ready data models, operationalizing ML systems, and enabling natural-language analytics through Databricks Genie or equivalent AI tooling.

This role exists to shift the organization from dashboard-driven analytics to AI-powered decision intelligence at enterprise scale.

Within 6–9 months, design and implement a scalable medallion-based architecture (Bronze/Silver/Gold) in Databricks or Snowflake that supports AI-ready datasets, improves query performance by ≥30%, and reduces data reliability incidents by ≥40%.

• Redesign analytical data models for AI/ML consumption
• Implement governance using Unity Catalog or Snowflake controls
• Establish monitoring and quality validation checkpoints

Within 6 months, establish semantic models and metadata standards that enable business-facing AI querying with ≥95% data trust rating from stakeholders.

• Standardize schema design for ML and GenAI workloads
• Align business definitions with governed datasets
• Implement lineage and access controls
• Reduce duplicate or conflicting metric definitions

• Standardize MLflow/Feature Store workflows
• Implement CI/CD for ML
• Improve model observability and drift monitoring

Within 6 months, deploy and optimize Databricks Genie (or equivalent AI query interface) enabling business users to generate accurate plain-language insights with ≥80% adoption across target user groups.

• Improve response accuracy through model + metadata tuning
• Partner with Product on use-case prioritization
• Track and improve AI query accuracy and user engagement

Within 12 months, embed AI-driven analytics into at least 3 core business workflows, demonstrating measurable business impact (e.g., cost reduction, revenue lift, or decision cycle time improvement).

• Identify high-value AI use cases
• Collaborate cross-functionally
• Deliver production-ready AI solutions
• Document business ROI outcomes

Within 12 months, define and institutionalize architectural standards, best practices, and governance frameworks adopted across Engineering and Analytics teams.

• Publish architecture reference patterns
• Influence long-term AI strategy

• 30%+ performance improvement in analytics workloads
• 40%+ reduction in data quality incidents
• 50% reduction in ML deployment cycle time
• 3+ AI use cases with measurable ROI
• ≥95% stakeholder trust in AI-generated insights

This is a high-impact platform leadership role enabling enterprise AI transformation. The individual will shape architecture standards, influence executive AI strategy, and lead the shift from traditional BI to AI-powered decision intelligence.

• 10+ years of experience in Data Analytics
  , Data Engineering
  , ML Engineering
  , or AI Engineering
• Strong hands-on experience with Databricks or Snowflake in production environments
• Expertise in SQL
  , Python
  , and distributed data processing (Spark preferred)
• Strong understanding of data modeling for analytics and AI
• Experience building and deploying ML models in real-world systems
• Familiarity with LLMs, GenAI concepts, and AI-assisted analytics
• Experience with ML lifecycle tools (MLflow, Feature Stores, CI/CD for ML)

• Direct experience with Databricks Genie or AI-powered BI tools
• Experience with Unity Catalog, Delta Live Tables
  , or Snowflake governance features
• Exposure to Azure, AWS, or GCP cloud platforms
• Experience working in regulated or enterprise SaaS environments
• Ability to explain complex technical concepts to non-technical stakeholders

• Business users can ask questions in plain English and get trusted, accurate insights
• Data models are AI-ready, scalable, and well-governed
• ML models move smoothly from experimentation to production
• Databricks Genie adoption grows with measurable business impact
• AI is embedded into analytics not bolted on

• Work on real AI/ML problems at enterprise scale
• Influence the evolution of a modern data + AI platform
• Partner with senior leaders shaping the company’s AI-first future
• Build systems that turn data into decisions not dashboards