AI Architect - Agentic Systems (LLM & Multi-Agent Solutions)

Responsibilities

• Design multi-agent architectures (task decomposition, orchestration, coordination patterns)
• Define how LLM-powered agents interact with:
• Enterprise data platforms
• APIs and tools
• Operational workflows
• Lead agentic systems from PoC to production with model, cost, security, privacy, and responsible AI guardrails
• Establish observability, tracing, feedback loops, and controls for agent behavior
• Define memory strategies (short-term, long-term, contextual grounding)
• Collaborate with data, platform, and engineering teams to integrate AI into core systems
• Guide and train teams on best practices for scalable and reliable AI systems

Requirements

• Core Experience
• Strong background in software architecture and distributed systems
• Hands-on experience building complex LLM-based applications
• Experience designing complex workflows or orchestration systems
• Solid understanding of RAG architectures, retrieval optimization, and retrieval quality
• LLM & AI Foundations
• Strong understanding of Large Language Model (LLM) fundamentals, not just usage
• Solid grasp of Transformer architecture (attention mechanisms, embeddings, tokenization)
• Understanding of how LLMs are trained and behave:
• Pretraining vs fine-tuning vs instruction tuning
• Context windows and limitations
• Hallucinations and mitigation strategies
• Familiarity with NLP concepts:
• Semantic similarity
• Text embeddings and vector representations
• Information retrieval principles
• Ability to reason about model behavior and limitations, not treat LLMs as black boxes
• Agentic / AI-Specific
• Experience with multi-agent frameworks (LangChain, Semantic Kernel, Agent Framework, CrewAI, or custom)
• Experience on platforms for managing the lifecycle of generative AI applications and agents like Amazon Bedrock (AWS), Google Vertex AI (GCP) or Azure AI Foundry
• Familiarity with protocols like: MCP, UCP, A2A, AP2
• Familiarity with:
• Tool use / function calling
• Agent coordination patterns
• Memory and context management
• Experience evaluating and improving LLM reliability and accuracy
• Engineering & Platform
• Cloud experience (AWS, Azure, or GCP)
• Strong coding skills (Python preferred)
• Experience integrating AI with enterprise systems (APIs, data platforms, event-driven systems)
• What Makes This Role Different
• Focus on systems, not isolated models
• Real enterprise use cases, not experimental demos
• Opportunity to define architecture patterns for agentic systems
• Work at the intersection of AI, data, and distributed systems
• Discover some of the global benefits that empower our people to become the best version of themselves:
• Finance: Competitive salary package, share plan, company performance bonuses, value-based recognition awards, referral bonus;
• Career Development: Career coaching, global career opportunities, non-linear career paths, internal development programmes for management and technical leadership;
• Learning Opportunities: Complex projects, rotations, internal tech communities, training, certifications, coaching, online learning platforms subscriptions, pass-it-on sessions, workshops, conferences;
• Work-Life Balance: Hybrid work and flexible working hours, employee assistance programme;
• Health: Global internal wellbeing programme, access to wellbeing apps;
• Community: Global internal tech communities, hobby clubs and interest groups, inclusion and diversity programmes, events and celebrations.

Benefits

Additional Information

We are looking for a highly skilled and hands-on AI Architect to design and lead the implementation of multi-agent (agentic) systems in enterprise environments. This is not a prompt engineering or chatbot role. We are focused on building production-grade AI systems, where multiple agents collaborate, reason, and execute complex workflows integrated with real business processes.

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