Artificial Intelligence Architect

Tata Consultancy Services; Pune District; On-site; INR 3,500,000 - 5,500,000; Full time

Job summary

Tata Consultancy Services is hiring for an AI Architect in Pune with 10-15 years of experience. The role focuses on designing robust AI/ML solution architectures aligned with business needs and enterprise tech standards.

The ideal candidate will apply Generative AI concepts, work with large language models, and architect scalable cloud-based AI solutions on AWS/Azure, while defining data patterns to support model development and operationalization.

Qualifications

• AI/ML architecture: design robust AI/ML solution architectures
• Generative AI/LLMs: apply generative AI concepts and prompt engineering
• Cloud architecture: deploy scalable AI solutions on AWS and Azure
• Data architecture: define data patterns to support model development and deployment

Skills

AI/ML architecture

Generative AI/LLMs

Cloud architecture

Data architecture

Job description

TCS is hiring for AI Architect

Role: AI Architect

Experience: 10-15 Years

Location: Pune

Desired Competencies (Technical/Behavioral Competency)

Must-Have:

• AI and Machine Learning Architecture: Design robust AI/ML solution architectures aligned with business and enterprise technology needs.
• Generative AI and LLMs: Apply Generative AI concepts, large language models, and prompt engineering techniques to build effective AI-driven solutions.
• Cloud Architecture: Architect and deploy scalable AI solutions on AWS and Azure cloud platforms.
• Data Architecture: Define data architecture patterns required to support AI/ML model development, integration, and operationalization.

Good-to-Have:

• Telecom Analytics: Experience applying analytics to telecom data, customer behavior, network insights, or business performance use cases.
• Big Data Engineering: Working knowledge of big data platforms and distributed processing frameworks for large-scale data handling.
• Spark/PySpark: Ability to build, optimize, and maintain data processing pipelines using Spark or PySpark.
• MLOps: Familiarity with model deployment, monitoring, versioning, and lifecycle management practices.
• Microservices: Understanding of microservices-based architecture and integration patterns for scalable enterprise applications.