AI Architect Job Description Page 1 Classification: Public 1. Role Overview The AI Architect is responsible for designing, governing, and evolving enterprise AI architectures that enable the adoption of Predictive AI, Generative AI, Agentic AI, and Intelligent Automation across the organization. The role provides architecture leadership throughout the AI solution lifecycle, ensuring that AI solutions are scalable, secure, responsible, compliant, and aligned to business outcomes and enterprise architecture standards. The AI Architect works closely with business stakeholders, Enterprise Architecture, Data Architecture, Security, Platform Engineering, Governance, Risk, Compliance, and delivery teams to define architecture patterns, review solutions, establish standards, and guide implementation. 2. Role Snapshot Profile Area Description Role Title Department AI Architect Primary Purpose AI Architecture | AI Center of Excellence Primary Stakeholders Design, govern, and evolve enterprise AI architectures across predictive AI, generative AI, agentic AI, intelligent automation, and related AI platform capabilities. Business teams, Enterprise Architecture, Data Architecture, Security, Platform Engineering, Risk, Compliance, Governance, and delivery teams. 3. Key Responsibilities 3.1 Architecture Strategy and Design Define and maintain enterprise AI reference architectures, blueprints, standards, and design patterns. Shape solution architectures for AI use cases across predictive, generative, agentic, conversational, document intelligence, and automation domains. Ensure alignment between business objectives, architecture principles, and technology capabilities. Define future-state AI architecture roadmaps and transformation initiatives. Evaluate architectural trade-offs involving scalability, resiliency, security, performance, and cost. 3.2 AI Solution Architecture Lead architecture design and solution shaping activities for AI initiatives. Define architectures spanning data, model, application, orchestration, integration, and infrastructure layers. Design patterns for RAG, agentic AI, multi-agent systems, prompt engineering, context engineering, AI orchestration, model serving, and knowledge management. Ensure AI solutions integrate effectively with enterprise platforms, APIs, data ecosystems, and operational processes. 3.3 Governance and Architecture Assurance Conduct architecture reviews and architecture governance checkpoints. Provide design authority and architecture sign-off for AI solutions. Ensure compliance with enterprise standards, security requirements, risk controls, and regulatory obligations. Page 2 Classification: Public Identify architecture risks, technical debt, and remediation opportunities. Maintain architecture decision records and solution governance documentation. 3.4 Responsible AI and Guardrails Define and implement architecture controls supporting Responsible AI. Ensure solutions incorporate content safety controls, hallucination mitigation, evaluation frameworks, human-in-the loop controls, explainability requirements, privacy controls, and data protection mechanisms. Partner with Risk, Compliance, Legal, Security, and Governance teams to embed controls into the AI lifecycle. 3.5 Platform and Technology Leadership Evaluate emerging AI platforms, tooling, and frameworks. Define technical direction for foundation models, agent platforms, vector databases, knowledge services, AI gateways, MLOps, LLMOps, AgentOps, and observability capabilities.



Establish reusable architecture capabilities and shared services. 3.6 Enterprise Architecture Alignment Act as the primary liaison between business teams and the AI Center of Excellence, translating business priorities into AI architecture direction and practical solution guidance. Ensure AI initiatives align with enterprise standards, target-state architecture, and strategic roadmaps. Support enterprise architecture forums and governance boards. Contribute to enterprise technology strategy and modernization initiatives. 3.7 Stakeholder Engagement and Leadership Collaborate with business leaders to translate business objectives into technology architectures. Facilitate architecture workshops, design sessions, and solution reviews. Mentor architecture analysts, solution architects, and engineering teams. Communicate architecture decisions to technical and non-technical stakeholders. 3.8 Innovation and Continuous Improvement Track emerging trends in AI, machine learning, foundation models, and enterprise AI platforms. Recommend architecture improvements and modernization opportunities. Drive adoption of reusable patterns and architectural best practices. Contribute to the evolution of the Enterprise AI Reference Architecture. 4. Skills and Experience 4.1 Technical Skills Strong experience designing enterprise-scale AI reference architectures across experience, agentic core, retrieval, orchestration, integration, models, data platform, and infrastructure layers. Deep understanding of predictive AI, generative AI, foundation models, agentic AI, RAG, vector and hybrid search, semantic layers, ontology management, knowledge graphs, enterprise search, and governed knowledge management. Ability to design agentic and application architectures including agent runtimes, reasoning loops, tool and function calling, multi-agent orchestration, workflow routing, memory, state management, and human-in-the loop controls. Page 3 Classification: Public Experience defining AI guardrails and responsible AI controls including content filtering, prompt-injection defense, PII detection and redaction, policy enforcement, safety classifiers, red-teaming, evaluation gates, audit logging, and traceability. Strong knowledge of integration and connectivity patterns including API gateways, service mesh, event and message streaming, connector catalogs, secure identity propagation, schema validation, traffic governance, quotas, and protocol mediation such as MCP and A2A. Understanding of model serving and lifecycle capabilities including model catalogs and registries, commercial and open-source model hosting, model routing and abstraction, inference infrastructure, fine-tuning, prompt libraries, benchmarking, and continuous evaluation. Experience with MLOps, LLMOps, and AgentOps practices including pipeline orchestration, model and prompt registries, CI/CD for models and agents, deployment and rollback, monitoring, drift detection, quality monitoring, and cost and usage metering. Strong Azure architecture skills across Azure landing zones, subscriptions, networking, private connectivity, identity and access management, policy, monitoring, cost management,



container platforms, AI services, data services, and secure cloud deployment patterns. Robust on-premises architecture skills across data center hosting, virtualized infrastructure, container platforms, GPU and accelerator capacity, storage, network segmentation, secrets management, secure connectivity, backup, resilience, patching, and operational controls. Ability to design hybrid AI deployment patterns spanning Azure and on-premises environments, including workload placement, data residency, latency, secure interconnect, identity federation, private endpoints, key management, monitoring, and failover considerations. Strong understanding of enterprise data platform capabilities including governed data products, feature and embedding stores, lineage, data quality controls, classification, retention, row- and column-level security, dynamic masking, and encryption. Strong understanding of infrastructure and platform capabilities including GPU and accelerator capacity, Kubernetes platforms, autoscaling inference, isolated networks, secrets management, storage, networking, hardened images, vulnerability scanning, observability, and capacity management. 4.2 Competency Matrix Competency Area Expected Capability AI & Data Architecture Predictive AI, machine learning, generative AI, foundation models, RAG, vector databases, semantic layers, knowledge systems, model serving, evaluation, and AI lifecycle practices. Governance & Risk Enterprise architecture, solution architecture, data architecture, integration architecture, platform architecture, security architecture, architecture governance, and design assurance. Delivery Leadership Responsible AI controls, risk management, privacy, security-by-design, compliance alignment, design sign off, technical debt management, and architecture decision records. Strategic Thinking Solution shaping, workshops, stakeholder engagement, cross-team coordination, technical decision-making, mentoring, and architecture communication. Future-state architecture, technology roadmap input, reusable patterns, innovation assessment, enterprise alignment, and business-value orientation. Page 4 Classification: Public 5. Qualifications Bachelor's degree in Computer Science, Information Technology, Information Systems, Computer Engineering, Software Engineering, or a related technical discipline. Preferred qualifications include a master's degree in AI, Data Science, Computer Science, Engineering, or equivalent practical experience, along with relevant architecture, cloud, or AI/ML certifications. 6. Experience 8 15+ years of overall technology experience. 5+ years in Solution Architecture, Enterprise Architecture, Data Architecture, Platform Architecture, or AI Architecture roles. Experience delivering enterprise-scale AI platforms, AI-enabled transformation initiatives, or complex AI solution architectures. Experience working within regulated, security-conscious, or governance-driven environments is highly desirable. 7. Success Profile Balances innovation with governance, risk management, and responsible AI adoption. Thinks strategically while remaining delivery focused. Drives architecture consistency, reuse, and standardization across initiatives. Influences stakeholders through architectural expertise, practical judgment, and collaboration. Ensures AI solutions deliver measurable business value and operational resilience. Champions responsible, secure, scalable, and sustainable AI adoption. Page 5 Classification: Public

📌 VIrtusa - AI Architect (India)
🏢 TRIGENT SOFTWARE PRIVATE
📍 India