AI Data Architect

Tata Consultancy Services; Bengaluru; On-site; INR 3,000,000 - 6,000,000; Full time

Job summary

Tata Consultancy Services in Bengaluru seeks a Data Architect for AI with 8-15 years of experience to lead enterprise-scale AI solutions, designing scalable data architectures on cloud platforms to support model training and inference.

You'll architect data lakes, ETL pipelines, and MLOps practices, collaborate with data scientists and stakeholders, ensure secure, reliable systems, and drive end-to-end data engineering initiatives across AWS/Azure/GCP.

Qualifications

• Bachelor’s or Master’s degree in Engineering or Technology.
• 8+ years of experience in ETL/ELT and Big Data cloud data solutions.
• At least 3 years in architectural leadership roles.
• Proven track record delivering enterprise Data solutions at scale.
• Strong understanding of Data models, data pipelines, and cloud-native architectures.

Responsibilities

• Architect scalable, secure data architectures in cloud to support AI/ML workloads.
• Build, optimize, and deploy end-to-end data solutions including processing engines.
• Develop data pipelines, ETL processes, Big Data analytics and data management.
• Choose and implement technologies like data lakes, batch/real-time processing, and MLOps.
• Collaborate with stakeholders to translate requirements into technical specs and ensure delivery.
• Ensure reliability, performance, and security of data & AI intensive systems.

Skills

Cloud platforms

Big data analytics

AI/ML knowledge

Python/SQL

Technical communication

Education

Bachelor's or Master’s in Engineering or Technology

Tools

Databricks

Snowflake

Hadoop

Spark

Job description

Role Overview: We are seeking an inventive Data Architect for AI with 8-15 years of experience to lead the strategic design and implementation of enterprise-scale AI solutions. This role requires deep expertise in designs, develops, and deploys scalable and secure data architectures on cloud platforms to support AI ML initiatives. They bridge the gap between business needs and technical implementation by creating the necessary infrastructure for data processing, model training, and inference. This role requires expertise in cloud services, distributed computing, that handle large datasets and complex workloads for AI/ML frameworks, and MLOps to build robust systems

Key Responsibilities:

• Architectural and Design: Create and document scalable, secure, and cost-effective data architecture in the cloud (AWS/Azure/GCP) to support AI/ML data workloads.
• Solution development: Build, optimize, and deploy end-to-end data solutions, such as recommendation data processing engines and data analytic engines.
• Data Engineering: Proficiency in Data pipelines, ETL processes, Big Data Analytics and data management (SQL, NoSQL, data cleaning).
• Technical implementation: Select and implement appropriate technologies, including data lakes, batch processing, real-time processing systems and MLOps tools.
• Collaboration: Work with stakeholders, data scientists, and other teams to translate business requirements into technical specifications and ensure successful technical delivery.
• System management: Ensure the reliability, performance, and security of Data & AI intensive systems.

Must-Have Skills:

• Cloud Platforms: Deep knowledge and expertise in cloud data services and platforms (AWS, Azure, and Google Cloud).
• Data and analytics: Experience in Big data platforms, Data modeling, and Distributed computing frameworks like Databricks, Snowflake, Hadoop or Spark.
• AI/ML knowledge: Experience in machine learning frameworks, platforms and MLOps (Machine Learning Operations) practices.
• Programming and scripting: Proficiency in languages like Python, Spark and SQL for data manipulation and system development.
• Technical communication: Strong ability to document architectures and communicate complex technical concepts to both technical and non-technical audiences.

Experience with any of the following Cloud Native Data Services:

• AWS: AWS Glue, AWS S3, AWS Athena, AWS Kinesis and AWS Redshift / EMR
• Google Cloud Platform (GCP): GCP Dataproc, GCP DataFlow, GCP BigQuery, GCP Cloud Storage, Cloud SQL and Pub Sub.
• Other public cloud platforms such as Snowflake, Hadoop…

Qualifications:

• Bachelor’s or Master’s degree in Engineering or Technology.
• 8+ years of experience in ETL/ELT, Big Data cloud data solutioning, with at least 3 years in architectural leadership roles.
• Proven track record of delivering enterprise Data solutions on a scale.
• Strong understanding of Data models and Data pipelines and cloud-native data architectures.

Excellent communication, stakeholder management, and leadershi