Solution Architect

Expertshub Ai; Delhi; On-site; INR 4,200,000 - 6,400,000; Full time

Job summary

Expertshub Ai in Delhi invites an AI Solution Architect to lead end-to-end architectures for secure, scalable government AI solutions across platforms. You will translate requirements into deployable designs spanning AI services, data platforms, APIs, cloud and on-premise infrastructure, and responsible AI controls.

We seek a leader with 10+ years in architecture roles and 5+ years in AI/ML systems, who can govern interoperability, security, and compliance while guiding cross-functional teams

Qualifications

• 10+ years in software/enterprise/architecture roles.
• 5+ years designing AI/ML systems or platforms.
• Experience delivering AI solutions in government/public sector.
• Ability to translate requirements into deployable technical designs.

Responsibilities

• Design end-to-end AI/ML solution architectures for government platforms.
• Define integration standards and API contracts with Digital India platforms.
• Own data flows, model lifecycle, security, privacy and observability.

Skills

Architectural leadership

Stakeholder management

Security & governance

AI ethics & Responsible AI

Education

PhD / MS in CS/AI

Cloud architecture certifications

Public sector knowledge

Tools

AWS Bedrock

SageMaker

Azure OpenAI Service

GCP Vertex AI

Job description

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 professional 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 governmental 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.