AI ML Architect

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)