AI Solution Architect

ROLE OVERVIEW
The AI Solution Architect will define and govern end-to-end architectures for secure, scalable, and interoperable AI solutions across NeGD and allied government platforms. The role will translate programme and business requirements into deployable technical designs spanning AI/ML services, data platforms, APIs, cloud and on-premise infrastructure, security, privacy, observability, and Responsible AI controls.

Educational Qualifications

- B.Tech., M.Tech., M.S., or Ph.D. in Computer Science, Information Technology, Artificial Intelligence, or a related technical discipline.
- Professional cloud architecture certifications such as AWS Certified Solutions Architect Professional, Microsoft Certified: Azure Solutions Architect Expert, or Google Cloud Professional Cloud Architect are preferred.
- Research publications, technical case studies, patents, or significant open-source contributions are preferred.

Experience

- 10+ years of total qualified experience in software, platform, cloud, data, or enterprise architecture roles.
- At least 5 years designing AI/ML systems, platforms, or products.
- Proven experience architecting and deploying multi-component AI solutions in government, public-sector, regulated, or large-enterprise environments.
- Experience integrating AI services with existing enterprise applications, data platforms, identity systems, APIs, and digital-service ecosystems.
- Demonstrated ability to evaluate architectural trade-offs across cloud-native, hybrid, on-premise, and sovereign deployment models.

Key Responsibilities

- Design end-to-end solution architectures that embed AI/ML capabilities into NeGD and allied government applications, covering data ingestion, model development, orchestration, serving, integration, monitoring, and user-facing services.
- Define integration standards, API contracts, interoperability patterns, and reference architectures for connecting AI systems with Digital India platforms and existing government applications.
- Own the architecture for data flows, model lifecycle, security, privacy,



auditability, observability, and Responsible AI controls across the solution landscape.
- Architect the integration of conversational AI, document intelligence, predictive analytics, computer vision, voice, and agentic services with enterprise applications and government digital platforms.
- Evaluate and recommend cloud-native, hybrid, sovereign-cloud, and on-premise deployment models in alignment with MeitY, NIC, data-residency, security, performance, and cost requirements.
- Provide architectural leadership to ensure scalability, resilience, maintainability, portability, performance, and compliance with applicable MeitY and NIC technical standards.
- Collaborate with data science, AI engineering, MLOps, application engineering, security, data governance, and product teams to establish unified architecture governance and delivery standards.
- Define reusable architecture patterns for RAG, semantic search, model gateways, prompt management, guardrails, human-in-the-loop workflows, and AI service observability.
- Review solution designs, technical specifications, infrastructure plans, and integration approaches; identify risks, dependencies, and remediation actions before deployment.
- Conduct periodic architecture reviews covering performance, scalability, reliability, security, privacy, cost, model risk, technical debt, and compliance.
- Support technology selection, vendor evaluation, proofs of concept, capacity planning, and cost optimisation for AI platforms and cloud services.
- Maintain architecture artefacts including current-state and target-state diagrams, solution blueprints, interface specifications, architecture decision records, threat models, and deployment standards.





Technical Competencies

- Solution Architecture: microservices, serverless, event-driven architecture, domain-driven design, distributed systems, API-led integration, high availability, disaster recovery, and multi-tenant architecture.
- AI/ML Architecture: end-to-end data and model lifecycle architecture, training and inference patterns, model gateways, LLM orchestration, RAG, vector search, prompt management, guardrails, evaluation, and Responsible AI practices.
- Cloud Platforms: AWS Bedrock, SageMaker, Lambda, API Gateway and S3; Azure OpenAI Service, Azure Machine Learning and AKS; GCP Vertex AI, BigQuery and Cloud Storage; multi-cloud integration and cost optimisation.
- AI Frameworks: TensorFlow, PyTorch, Hugging Face Transformers, LangChain or equivalent orchestration frameworks, and model-serving patterns.
- Data and Storage: PostgreSQL, MySQL, MongoDB, DynamoDB, object storage, data lakes/lakehouses, and vector databases such as Pinecone, Weaviate, Milvus, or equivalent.
- APIs and Integration: REST, GraphQL, WebSocket, event streaming, service mesh, API gateways, identity federation, and real-time integration patterns for chat and voice applications.
- Conversational AI and Voice: RAG/chatbot pipeline architecture, speech-to-text and text-to-speech services including AWS Transcribe/Polly and Azure Speech, conversation state, latency, and channel integration.
- Security and Governance: IAM, RBAC/ABAC, secrets management, encryption in transit and at rest, network segmentation, zero-trust principles, secure model access, data privacy, audit logging, and AI governance.
- Standards and Compliance: working knowledge of MeitY and NIC guidelines, DPDPA 2023, GDPR, SOC 2 principles, security-by-design, privacy-by-design, and Responsible AI controls.
- Architecture Governance: architecture review boards, reference architectures, architecture decision records, non-functional requirements, threat modelling, technical risk management, and stakeholder communication.

📌 Solution Architect (New Delhi)
🏢 Expertshub.ai
📍 New Delhi