AI Technical Architect

We’re looking for an AI Technical Architect to lead the design and delivery of cutting-edge Generative AI and Agentic AI solutions for major global organisations. This is a senior, client-facing role where you’ll combine deep technical expertise, architecture leadership, and strategic advisory to shape high-impact AI transformations.

The Role

As an AI Architect, you will play a key role in shaping enterprise AI strategy and delivering innovative solutions across large-scale environments.

Key Responsibilities

AI Solution Architecture

• Design and architect enterprise-grade GenAI, Agentic AI and ML solutions aligned to client business goals.

• Build scalable architectures using modern cloud platforms (Azure, AWS or GCP).

• Lead the design of RAG-based solutions, AI assistants, and autonomous agent systems.

Technical Leadership

• Guide the end-to-end delivery of complex AI implementations.

• Evaluate and select models, frameworks and orchestration tools.

• Ensure solutions are scalable, secure and production-ready.

Client Advisory

• Act as a trusted advisor to senior stakeholders during workshops, discovery sessions and transformation programmes.

• Support pre-sales activities including solution shaping, proposals and business cases.

Innovation & Strategy

• Contribute to AI thought leadership and go-to-market propositions.

• Identify opportunities to optimise operations, improve productivity and unlock new value through AI.

Your Experience

We’re looking for a strong mix of AI architecture expertise, hands-on engineering knowledge and consulting capability.

Key Skills & Experience

• Proven experience architecting Generative AI, Agentic AI, machine learning and automation solutions in enterprise environments.

• Strong expertise in LLMs and RAG architectures, including models such as OpenAI, LLaMA, Mistral or Claude.

• Hands-on experience with GenAI frameworks such as LangChain, LlamaIndex, AutoGen, CrewAI or LangGraph.

• Experience designing AI systems using vector databases (e.g. Pinecone, Weaviate, FAISS).

• Solid understanding of prompt engineering, model tuning, and AI evaluation methodologies.

• Experience building production-grade AI systems using Python and ML frameworks (PyTorch, TensorFlow).

• Knowledge of MLOps / LLMOps practices and tooling (MLflow, Kubeflow, Docker, Kubernetes).

• Experience delivering AI solutions on AWS, Azure or GCP

• Experience leading or mentoring teams of AI engineers or data scientists.

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