AI Architect - Agentic AI & Enterprise Platforms

As an AI Architect focused on agentic AI and enterprise platforms, you shape how organizations design and implement advanced AI solutions. In Goteborg, you independently own architecture for defined solutions or platform workstreams, collaborating onsite with client enterprise architects and specialists in security, identity, data and integration. You translate business needs into practical architectures, evaluate technology choices, and guide delivery from initial design through production adoption. This role matters because it enables enterprise clients to realize the value of agentic AI through robust, scalable and secure platforms. Responsibilities Translate business use cases and constraints into architecture requirements, measurable success criteria and an achievable implementation roadmap Develop target architectures, integration designs, and reusable patterns for AI solutions and platform capabilities, aligned with enterprise standards Evaluate models, agent frameworks, orchestration approaches, and enterprise platforms against quality, security, interoperability, reliability, latency and cost requirements Collaborate with domain specialists to address identity, authorization, data access, tool integration and interoperability across vendor platforms and existing systems Build focused prototypes or reference implementations, review technical designs and help engineers resolve architectural issues as solutions move into production Define evaluation criteria, observability, human oversight, failure handling and operational controls with engineering, security and responsible-AI teams Facilitate architecture workshops, reconcile input from client teams and vendors and document decisions, trade-offs, dependencies and implementation guidance Guide teams in applying agreed patterns and standards, mentor engineers on architecture and use delivery feedback to improve the design Translate relevant developments in agentic AI into practical recommendations grounded in client needs and implementation evidence Requirements Seven years of experience in software engineering, cloud platforms, data/AI engineering or solution architecture, including two years owning architecture or leading technical design Two years designing or delivering AI solutions, including recent hands-on work with LLM-based applications Architecture or technical design ownership of at least one LLM-based solution deployed to production, with the ability to explain integration choices, evaluation results and operational lessons Practical experience with model selection, retrieval-augmented generation (RAG), tool use and agent orchestration, including the judgment to choose between agentic workflows and simpler approaches Experience designing solutions in a complex enterprise environment; strong knowledge of at least one cloud or enterprise AI platform, APIs, integration patterns, identity and access control Proficiency in Python or another relevant language sufficient to build prototypes, review implementation choices and investigate integration issues Practical understanding of evaluation, deployment, monitoring and cost management, together with data protection, prompt-injection risks, tool permissions and human oversight Ability to produce clear diagrams, decision records and implementation guidance; facilitate client workshops; and explain technical trade-offs to engineers and business stakeholders Ability to structure ambiguous requirements, work across teams and vendors and build agreement without formal authority, while escalating decisions beyond the workstream's scope Nice to have Experience with multiple enterprise AI platforms, shared agent services or centralized capabilities for agent registration, policy enforcement, and monitoring Familiarity with Model Context Protocol (MCP), agent-to-agent interoperability, tool gateways, execution sandboxing, agent identity or knowledge graphs Experience turning architecture patterns into adopted standards across teams in a large or federated enterprise Familiarity with responsible-AI governance and the EU AI Act, and experience working with legal, risk or compliance specialists Experience in automotive, manufacturing or similarly complex enterprise environments