AI Automation Architect

Cognizant; Greater London; Hybrid; GBP 120,000 - 180,000; Full time

Job summary

Cognizant is seeking a senior architect to own end-to-end solution design for AI-enabled automation initiatives. You will lead architecture for AI/agentic workflows, cloud integration, and governance, while mentoring delivery teams and engaging client stakeholders.

You will orchestrate LLM patterns, agent frameworks, and decision logic, and drive migration from legacy automation toward AI-augmented delivery within a cloud-first environment.

Qualifications

• Strong solution or enterprise architecture background with AI/agentic systems experience.
• Hands-on understanding of LLM orchestration and prompt design at an architectural level.
• Cloud platform experience across Azure/AWS/GCP with architecture certification preferred.
• Experience defining governance, monitoring, risk controls for production AI systems.
• Excellent client-facing and stakeholder management abilities.

Responsibilities

• Lead architecture design for AI/agentic workflows and cloud integration.
• Translate business problems into target-state technical designs.
• Define LLM orchestration patterns, agent frameworks, and decision logic.
• Set cloud-native architecture for automation and AI workloads.
• Establish technical standards, governance, and best practices.
• Own operational strategy for AI systems in production, including monitoring and deployment gates.
• Act as primary technical contact with client stakeholders for reviews and governance boards.
• Mentor AI/Automation Engineers and Integration Architects on the account.
• Evaluate build-vs-buy decisions across RPA platforms and AI tooling.
• Assess migration paths from legacy automation to AI-enabled delivery.
• Provide technical risk assessment and escalation as needed.
• Support pre-sales architecture input and review engineering designs for alignment.

Skills

LLM orchestration

Cloud architecture

RPA/BPM to AI transition

Stakeholder management

Technical leadership

Architecture governance

Tools

LangChain

Agent SDKs

Azure

AWS

GCP

Job description

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

Required Skills/Experience

• 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