Data Architect, Generative AI & Data Platform

Role Summary : The

Principal Architect, Generative AI & Data Platforms

is a strategic role responsible for architecting and operationalizing the end-to-end data ecosystem that powers

Generative AI, Machine Learning (ML), and advanced analytics . This role focuses on building a robust, scalable, and compliant platform—leveraging modern cloud and Gen AI techniques (like RAG)—to transform raw data into a reliable, secure asset for data scientists, analysts, and business intelligence users.

Experience : 12 - 18 years

Location : Chennai

Core Responsibilities

Gen AI / ML & Data Platform Strategy :

Define and lead the technical roadmap for data architecture, specifically optimizing data storage, indexing (e.g., Vector Stores), and retrieval patterns for Gen AI and RAG (Retrieval Augmented Generation) pipelines.

Cloud & Big Data Architecture :

Design and manage highly available, performant, and cost-optimized data solutions across major cloud platforms (AWS, Azure, GCP). Utilize Big Data technologies (Spark, Databricks, Snowflake, Kafka) for processing petabyte-scale data volumes.

Data Modeling for Intelligence : Create advanced data models (e.g., Dimensional, Data Vault) and semantic layers specifically tailored to accelerate ML model training, feature engineering, and real-time analytical queries.

Data Governance & Trust :

Implement strict Data Governance, lineage, and privacy frameworks, ensuring the ethical and secure use of data in all AI / ML initiatives. Establish robust Data Quality and auditing mechanisms.

MLOps & Deployment Alignment :

Directly collaborate with MLOps and Data Science teams to streamline the flow of data from ingestion to model training, versioning, and high-volume inference deployment.

API & Data Integration :

Architect secure, high-speed data ingestion and egress mechanisms, including Real-Time Data Streams and Microservices-based APIs, to integrate diverse internal and external data sources.

Leadership & Technical Evangelism :

Act as the subject matter expert on data architecture across the organization. Effectively communicate complex technical vision and trade-offs to c-suite and non-technical stakeholders

Required Skills & Qualifications

Technical Expertise :

Proven experience designing and implementing scalable Gen AI / ML data architectures, Vector Databases, and RAG patterns. Expert-level proficiency in data modeling (conceptual, logical, physical) and Big Data processing tools (Spark, Databricks, DWH).

Cloud Mastery :

Deep hands-on experience architecting data solutions on at least one major cloud platform (AWS, Azure, or GCP), including native data services (e.g., AWS S3 / Glue, Azure Data Lake / ADF, GCP BigQuery / Dataproc).

Data Lifecycle :

Strong understanding of the complete Data Engineering lifecycle (ELT / ETL, CI / CD, and monitoring), real-time streaming, and API integration techniques.

Soft Skills :

Exceptional communication, stakeholder management, and cross-functional collaboration skills. Ability to mentor and lead technical teams.

Education : Bachelor's degree in Computer Science, Engineering, or a related quantitative field. An advanced degree is strongly preferred.