Cognizant
AI Automation Architect
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Key Purpose
Own end-to-end solution architecture for AI-enabled automation engagements, moving delivery beyond traditional rule-based RPA into agentic, cloud-native automation. Act as the primary technical authority and client-facing lead across engagements, responsible for solution design, technical governance, and the transition of legacy automation practices toward AI-augmented delivery.
Roles and Responsibilities
- Lead architecture design for solutions spanning AI/agentic workflows, process automation, and cloud integration — translating business process problems into target-state technical designs
- Design LLM orchestration patterns, agent frameworks, and decision logic for automation use cases, as distinct from purely deterministic RPA flows
- Define cloud-native architecture (Azure/AWS/GCP) for automation and AI workloads, including compute, storage, networking, and security considerations
- Set and enforce technical standards, governance, and best practices across engagements and delivery teams
- Own operational strategy for AI systems in production: define monitoring, observability, deployment governance, and model-risk standards that engineering teams implement
- Act as primary technical point of contact with client stakeholders — running architecture reviews, solution walkthroughs, and technical governance boards
- Mentor and provide technical direction to AI/Automation Engineers and Integration Architects on the account
- Evaluate build-vs-buy and tooling decisions across RPA platforms, LLM providers, and agent frameworks
- Assess existing RPA estates and define migration/uplift paths toward AI-enabled automation where relevant
- Own technical risk assessment and escalation for architecture decisions with client and internal leadership
- Support pre-sales/solutioning activity where technical architecture input is required
- Review and sign off engineering designs before build to ensure alignment with architecture standards
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Required Skills/Experience
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- Strong solution or enterprise architecture background, ideally with prior RPA/BPM experience now extended into AI/agentic systems
- Hands-on understanding of LLM orchestration, agent frameworks (e.g., LangChain, agent SDKs), and prompt/context engineering at an architectural level
- Deep cloud platform experience across Azure/AWS/GCP; architecture-level certification preferred
- Demonstrated experience defining governance/standards for production AI systems, including monitoring, risk controls, and deployment gates
- Strong client-facing and stakeholder management skills; comfortable leading technical governance conversations with senior client stakeholders
- Prior technical leadership of engineering teams, including design review and mentoring
- Experience evaluating and selecting automation/AI tooling and platforms
- Track record of migrating or modernizing legacy automation estates
- Strong communication skills — able to translate technical architecture into business-relevant language for non-technical stakeholders
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