Technology Architect

Accenture

Chennai District

On-site

INR 1,000,000 - 1,500,000

Full time

14 days+

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Benefits offered by this job

Ability to work in a team environment

Professional development opportunities

Job summary

Accenture is seeking a Technology Architect based in Chennai with a focus on designing and delivering technology architecture for AI/ML applications. The role requires strong experience in Google Cloud Data Services and should have a proven track record in building scalable data pipelines.

The ideal candidate will possess a minimum of 9 years in data engineering, excellent programming skills, and familiarity with cloud-based solutions. Additional benefits include a collaborative team environment and opportunities for professional development.

Qualifications

Minimum experience of 9 years in Google Cloud Data Engineering or related domains.
Strong educational background in computer science, data engineering, or a related field.
Expertise in building and scaling data systems for AI/ML initiatives.

Responsibilities

Design and develop scalable data pipelines and ETL processes.
Collaborate with data scientists and ML engineers for data ingestion and model serving.
Support GenAI/Vertex AI model deployment ensuring reliable data access.

Skills

Google Cloud Platform Architecture

Data Engineering

AI/ML

Python

SQL

Education

Bachelor's degree in Computer Science or related field

Tools

BigQuery

Dataflow

Pub/Sub

Data Engineering Pipelines

Job description

Job Description

Project Role: Technology Architect

Project Role Description: Design and deliver technology architecture for a platform, product, or engagement. Define solutions to meet performance, capability, and scalability needs.

Must have skills: Google Cloud Platform Architecture

Good to have skills: Google BigQuery

Minimum experience: 7.5 years

Educational Qualification: 15 years full time education

Summary: As a Data Engineer - AI/ML, you will be responsible for designing, building, and maintaining scalable data pipelines and systems that power AI/ML applications on Cloud platforms. Your typical day will involve leveraging Google Clouds data services, implementing GenAI and AI/ML models, and supporting data-driven solutions through efficient architecture and engineering.

Roles & Responsibilities

Design and develop scalable data pipelines and ETL processes using Google Cloud data services like BigQuery, Dataflow, Pub/Sub, and Dataproc.
Build and optimize data architectures to support AI/ML applications and model training at scale.
Collaborate with data scientists and ML engineers to implement data ingestion, feature engineering, and model‑serving pipelines.
Develop and manage data integration solutions that align with enterprise data governance and security standards.
Support GenAI/Vertex AI model deployment by ensuring reliable data access and transformation pipelines.
Implement monitoring, logging, and alerting for data workflows and ensure data quality across all stages.
Enable self‑service analytics by building reusable data assets and data marts for business stakeholders.
Ensure cloud‑native, production‑grade data pipelines and participate in performance tuning and cost optimization.
Experience with programming languages such as Python, SQL, and optionally Java or Scala.

Professional Technical Skills

Must To Have Skills: Strong experience in Google Cloud Data Services (BigQuery, Dataflow, Pub/Sub) and hands‑on with scalable data engineering pipelines.
Good To Have Skills: GenAI/Vertex AI exposure, Cloud Data Architecture, PCA/PDE certifications.
Understanding of data modeling, data warehousing, and distributed computing frameworks.
Experience with AI/ML data pipelines, MLOps practices, and model deployment workflows.
Familiarity with CI/CD and infrastructure‑as‑code tools (Terraform, Cloud Build, etc.) for data projects.

Additional Information

The candidate should have a minimum of 9 years of experience in Google Cloud Data Engineering or related domains. The ideal candidate will possess a strong educational background in computer science, data engineering, or a related field, along with a proven track record of building and scaling data systems for AI/ML initiatives.

Job Classification