Principal Solution Architect - AI/ML

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 12+ years of total professional experience.
• At least 5 years in AI/ML system design
• 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 NeGDs 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:

• 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, Big Query, Cloud Storage), multi-cloud integration & cost optimization
• AI Frameworks: TensorFlow, PyTorch, Hugging Face Transformers, Lang Chain (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)