AI ML Architect

Tata Consultancy Services; Dadri, New Delhi; On-site; INR 3,500,000 - 5,500,000; Full time

Job summary

Tata Consultancy Services is seeking an AI/ML Solution Architect to lead end-to-end architectural design for large-scale AI deployments in government and enterprise settings.

The role requires strong expertise in microservices, cloud platforms, and responsible AI practices, with experience across Data Science, MLOps, and engineering teams to ensure scalable, secure architectures.

Qualifications

• B.Tech / M.Tech / MS / Ph.D. in Computer Science, Information Technology, Artificial Intelligence, or related field.
• Certifications preferred: AWS Solutions Architect Professional, Azure Solutions Architect, GCP Professional Cloud Architect.

Responsibilities

• Design end-to-end AI/ML solution architectures embedding capabilities into NeGD and allied government applications.
• Define integration standards, APIs, and interoperability frameworks between AI systems and existing Digital India platforms.
• Oversee data flow architecture, security, privacy, and Responsible AI implementation across solutions.
• Architect and oversee integration of AI applications with NeGD’s current enterprise applications and digital platforms.
• Evaluate and recommend cloud-native and on-premises deployment models in alignment with MeitY guidelines.
• Provide architectural leadership ensuring scalability, maintainability, and compliance with MeitY and NIC standards.
• Collaborate with Data Science, MLOps, and Engineering teams for unified architecture governance.
• Conduct periodic architectural reviews and provide technical mentoring to development teams.

Skills

AI/ML architecture

Microservices

Serverless

Event-driven design

Domain-driven design

Data lifecycle

Cloud platforms

TensorFlow

PyTorch

Hugging Face

LangChain

REST

GraphQL

WebSocket

Speech-to-Text

Text-to-Speech

IAM RBAC

Data privacy

Education

B.Tech / M.Tech / MS / Ph.D. in CS/IT/AI

Tools

AWS Bedrock

SageMaker

Azure OpenAI

AKS

Vertex AI

GCP Cloud Storage

Job description

AI/ML Solution Architect

Designation: AI/ML Solution Architect

Educational Qualification:

• B.Tech / M.Tech / MS / Ph.D. in Computer Science, Information Technology, Artificial

Intelligence, or a related technical field.

• Certifications preferred: AWS Solutions Architect Professional, Azure Solutions Architect

Expert, or GCP Professional Cloud Architect.

• Research papers, case studies, or significant open source contributions(are preferred)

Experience:

• Minimum 812 years of total professional experience.


• At least 4–5 years in architecting enterprise-level AI/ML solutions or large-scale data

systems.

• Proven experience designing and deploying multi-component AI solutions for government or

enterprise environments.

Key Responsibilities:

1. Design and implement end-to-end solution architectures embedding AI/ML capabilities into

NeGD and allied government applications.

2. Define integration standards, APIs, and interoperability frameworks between AI systems and

existing Digital India platforms.

3. Oversee data flow architecture, security, privacy, and Responsible AI implementation across

solutions.

4. Architect and oversee integration of AI applications with NeGD’s current enterprise

applications and digital platforms.

5. Evaluate and recommend cloud-native and on-premises deployment models in alignment with

MeitY guidelines.

6. Provide architectural leadership, ensuring scalability, maintainability, and compliance with

MeitY and NIC technical standards.

7. Collaborate with Data Science, MLOps, and Engineering teams for unified architecture

governance.

8. Conduct periodic architectural reviews and provide technical mentoring to development

teams.

Technical Competencies:

86

• Architecture: Microservices and serverless patterns, event-driven and domain-driven design,

high-level data & model lifecycle architecture

• Cloud: AWS (Bedrock, SageMaker, Lambda, API Gateway, S3), Azure (OpenAI Service, ML

Studio, AKS), GCP (Vertex AI, BigQuery, Cloud Storage), multi-cloud integration & cost

optimization

• AI Frameworks: TensorFlow, PyTorch, Hugging Face Transformers, LangChain (LLM

orchestration), prompt-engineering and responsible AI practices

• Data & Storage: SQL (PostgreSQL, MySQL), NoSQL (MongoDB, DynamoDB), vector

databases (Pinecone, Weaviate) for RAG & semantic search

• APIs & Integration: REST, GraphQL, WebSocket for real-time chat/voice


• Conversational AI & Voice: Speech-to-Text and Text-to-Speech APIs (AWS

Transcribe/Polly, Azure Speech), RAG/chatbot pipeline design with vector stores & LLMs

• Security & Governance: IAM and RBAC across clouds, encryption in transit & at rest, data

privacy & AI governance (GDPR, SOC2)