Agentic AI Architect

Job Title: Agentic AI Architect Job Location: London,UK Job Duration: Full Time Exp: 10+ Yrs We at Coforge are hiring for Agentic Architect with the following skillset Role Purpose Design enterprise-scale AI products and agentic systems that combine models, data, workflows, platforms, and governance into secure, reusable, and commercially viable solutions. Key Responsibilities

• Define end-to-end architecture for enterprise AI products and agentic platforms.
• Translate product strategy and domain needs into target architecture, roadmaps, and reusable patterns.
• Select models, orchestration approaches, data patterns, integration methods, and deployment topology.
• Design security, privacy, identity, auditability, resilience, and human-oversight controls.
• Guide multiple engineering teams and review critical technical decisions.
• Partner with product, delivery, platform, governance, and enterprise architecture leaders.
• Assess technology options, technical debt, scalability, cost, and vendor dependencies. Required Skills
• Enterprise and solution architecture
• Generative AI, agentic systems, RAG, knowledge architecture, and model strategy
• Cloud-native platforms, APIs, event-driven integration, and data architecture
• Identity, security, privacy, governance, and responsible AI
• Architecture modelling, trade-off analysis, and technology roadmapping
• Platform reuse, non-functional design, and cost optimisation Qualifications
• Bachelor's or master's degree in a relevant technical discipline.
• 10–14 years in architecture, engineering, data, or platform roles.
• Experience architecting production AI, digital, or cloud platforms at enterprise scale.
• Experience influencing senior technology and business stakeholders. Behavioural Competencies
• Strategic thinking
• Architecture leadership
• Commercial and product mindset
• Clear executive communication
• Decision-making under ambiguity
• Strong governance and risk judgement Performance Measures
• Architecture adoption and reuse
• Scalability, reliability, and security outcomes
• Time-to-market and technical-debt reduction
• Platform and model cost efficiency
• Compliance with enterprise standards
• Business value delivered by AI products