Databricks Solution & AI Architect

Job Title : Databricks Solution & AI Architect
Job location : Bangalore / Chennai
Job requirement : Fulltime / Permanent
Experience : 14+ years of experience
Work Mode : Hybrid

Looking for a senior Databricks architect with deep expertise in Lakehouse platform design, data engineering at scale and AWS cloud integration. This role leads solution design and delivery of enterprise data products with hands-on capability to implement AI/GenAI features where they add clear business value. The right candidate owns the full data platform: from medallion architecture and Unity Catalog governance to performance-tuned pipelines and stakeholder-facing analytics. AI capability is a meaningful differentiator, not the primary mandate applied where data readiness and ROI are established.

Primary Responsibilities

Core Platform & Architecture (Primary Focus)
Lead end-to-end design of Databricks Lakehouse solutions medallion architecture (Bronze/Silver/Gold) optimised for performance, cost, and downstream consumption.
Design and build production-grade data pipelines using PySpark, Databricks SQL, Auto Loader, and Delta Live Tables.
Architect event-driven and batch ingestion frameworks with strong SLA management, data quality rules, and lineage tracking.
Define Data Product blueprints: domain boundaries, ownership, contracts, metadata standards, and discoverability.
Lead Unity Catalog implementation fine-grained permissions, data lineage, row/column security, and cross-workspace governance.
Own AWS integration: S3 bucket design, IAM roles, VPC networking, and secure credential management for Databricks workspaces.
Drive DevOps practices using Git, Databricks CLI/SDK, Repos, and CI/CD pipelines for infrastructure and pipeline deployments.
Optimise Spark workloads using Photon, Serverless compute, and cluster tuning for cost efficiency.
Build and publish Power BI dashboards DAX measures, data models, DirectQuery/Import mode for business stakeholders.
Lead architecture reviews, performance tuning, and code quality standards across engineering teams.
Evaluate new Databricks platform features and integrate them into the technical roadmap.

AI / GenAI Enablement (Supporting Focus)
Implement Databricks AI/BI Genie spaces to enable natural language querying for non-technical business users.
Design and deploy RAG pipelines using Databricks Vector Search integrated with Delta Lake for real-time data freshness.
Productionise ML models and AI agents using MLflow, Feature Store, and Mosaic AI Model Serving.
Support AI use case discovery workshops identifying where data is AI-ready and scoping feasibility of RAG or Agentic workflows.
Implement LLMOps basics: monitoring, evaluation loops, and CI/CD for AI model assets.
Apply Unity Catalog governance to AI assets — model permissions, vector index access controls, and audit trails.

Technical Skills Required

1. Databricks & Data Engineering
PySpark, SQL, Python — production pipeline mastery
Delta Lake, Auto Loader, Delta Live Tables
Unity Catalog — governance, lineage, security
Databricks Workflows & SQL Warehouses
Medallion architecture & Data Mesh principles
Performance tuning: Photon, Serverless, caching
Databricks CLI/SDK, Repos, CI/CD integration
Data Product design: contracts, versioning, SLAs
Power BI: DAX, DirectQuery/Import, data models AWS Cloud & DevOps
S3, IAM, VPC, Glue, Lambda, Step Functions
CloudWatch — monitoring, alerts, observability
Terraform — workspace, clusters, UC policies, jobs
Git-based CI/CD for infra and pipelines

2. AI / GenAI (Working Knowledge)
Mosaic AI, MLflow, Feature Store, Model Serving
Databricks Vector Search & RAG pipeline design
AI/BI Genie — space setup and query tuning
LLM integration basics (Bedrock / Model Serving)
AI code assistants: Copilot, Gemini, Claude Code

Qualifications
8–12 years of professional experience in Data Engineering and cloud architecture.
Minimum 4+ years hands-on with Databricks at production scale.
Demonstrated delivery of Data Products on cloud Lakehouse platforms (AWS preferred).
Proven experience leading RFP technical tracks, scoping complex projects, and producing effort estimations.
1–2 years exposure to productionising AI/ML solutions on Databricks is a strong advantage.

Soft Skills
Strong ownership mindset — takes end-to-end accountability for platform reliability and data quality.
Excellent communication: able to translate complex architecture into clear business value for C-suite and product stakeholders.
Engineering leadership: mentors teams, drives code standards, and influences architectural decisions at an enterprise level.
Pragmatic approach to AI — champions use cases where data readiness exists; avoids over-engineering.
Continuous innovation mindset with focus on automation, performance optimization, and platform reusability.