Coforge
Agentic AI Architect
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Agentic AI Architect
Job Location: London, UK
Job Duration: Full Time
Experience: 10+ Years
We at Coforge are hiring for an Agentic AI 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.
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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.
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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
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