Key Responsibilities

- Design end-to-end data architecture, data platforms, and AI solutions for enterprise applications.
- Architect modern data warehouses, data lakes, and lakehouse platforms.
- Design scalable data engineering and ETL/ELT pipelines for structured and unstructured data.
- Define data models, data integration patterns, data governance, metadata, and data quality frameworks.
- Develop solutions for data ingestion, transformation, processing, and analytics.
- Architect and implement AI/ML and Generative AI solutions using enterprise data.
- Design RAG (Retrieval-Augmented Generation) architectures using enterprise documents and structured data.
- Work with LLMs, vector databases, embeddings, prompt engineering, and AI orchestration frameworks.
- Evaluate and select appropriate cloud data and AI services based on business requirements.
- Ensure solutions meet requirements for security, scalability, performance, reliability, and cost optimization.
- Establish best practices for data governance, privacy, lineage, security, and lifecycle management.
- Collaborate with data engineers, data scientists, ML engineers, software engineers, and business stakeholders.
- Provide technical leadership and mentor engineering teams on data and AI architecture.

Required SkillsData Architecture & Warehousing

- Strong experience in Data Warehousing and Data Architecture.
- Expertise in dimensional modeling, data modeling, data marts, and enterprise data platforms.
- Strong understanding of Data Lakes, Lakehouse architecture, and contemporary data platforms.
- Experience with technologies such as Snowflake, Databricks, BigQuery, Redshift, Synapse, or similar platforms.

Data Engineering
- Strong experience with ETL/ELT pipelines and data integration.
- Hands-on experience with SQL and Python.




- Experience with Apache Spark, Kafka, Airflow, dbt, or similar technologies.
- Understanding of batch and real-time/streaming data processing.
- Experience integrating APIs, databases, files, and cloud data sources.

Data Management
- Strong understanding of data governance, data quality, metadata management, data lineage, and master/reference data management.
- Experience defining data standards, security controls, and data lifecycle policies.
- Knowledge of enterprise data management frameworks and best practices.

AI & Generative AI
- Strong understanding of AI/ML concepts and Generative AI architectures.
- Experience working with LLMs, RAG, embeddings, vector databases, and prompt engineering.
- Experience with frameworks/tools such as LangChain, LangGraph, LlamaIndex, or equivalent.
- Experience integrating OpenAI, Azure OpenAI, Google Vertex AI, AWS Bedrock, or equivalent GenAI platforms.
- Understanding of AI agents/Agentic AI, model evaluation, guardrails, and responsible AI is preferred.

Cloud & Architecture
- Experience with at least one major cloud platform: AWS, Azure, or GCP.
- Strong understanding of cloud-native architecture, APIs, microservices, security, and CI/CD.
- Ability to design highly scalable, secure, and cost-effective enterprise solutions.

Qualifications

- 8+ years of overall experience in data engineering, data architecture, AI/ML, or related technology domains.
- 3+ years of experience in data architecture/data engineering.
- Hands-on experience designing and implementing enterprise data platforms.
- Experience delivering AI/GenAI solutions in production environments.
- Strong architecture, problem-solving, communication, and stakeholder-management skills.

Skills: data architecture,data,design,enterprise data,architecture,enterprise,data engineering,ai

📌 Data & AI Architect (Chennai)
🏢 CG-VAK Software u0026 Exports
📍 Chennai