AI Architect - GenAI & Agentic Systems

Drive Innovation in GenAI and Automation

We are looking for an AI Architect/Lead (Grade 140) to design, build, and deploy automation and Generative AI solutions end to end, from architecture and prototypes to production systems with strong post-deployment monitoring. You will work with cross-functional teams to drive agentification and shape platform capabilities used across the organization.

• →Architect end-to-end AI and automation solutions on cloud-native platforms, from design through production.
• →Build agentic systems: LLMs and other Generative AI models (diffusion, audio/video), RAG pipelines, and multi-agent orchestration with frameworks such as LangChain, LangGraph, and MCP.
• →Build agent harnesses: tool orchestration, memory, verification loops, and guardrails such as permissions, sandboxing, and human approval for risky actions.
• →Develop automation frameworks, and apply prompt and context engineering (retrieval, memory, context compaction) to improve accuracy, grounding, and relevance.
• →Deploy through scalable CI/CD pipelines on Azure and/or AWS, with safe rollout and rollback.
• →Monitor after release, with observability for traces, logs, latency, errors, token usage, and cost.
• →Measure and improve through evaluation-driven development: quality and agent metrics, task suites, LLM-as-judge, regression tests on every prompt, model, or tool change, and benchmarking of cloud AI services.
• →Partner with engineering, product, and business stakeholders on prototypes and demos, and drive adoption.

• Strong proficiency in Python and hands-on development experience.
• Hands-on experience with Azure and/or AWS.
• Experience building backend services and APIs, with automated tests around them.
• Experience with CI/CD and safe rollout of services, including secrets management, identity, and environment promotion.

• Practical experience with Generative AI, including LLM-based systems.
• Strong understanding of RAG architectures, embeddings, vector databases, and grounding strategies.
• Experience with LangChain, LangGraph, or similar GenAI orchestration frameworks.
• Experience with AI agents, multi-agent systems, and protocols such as MCP and A2A.
• Experience with LLM and agent observability and evaluation (LangSmith, OpenTelemetry, or similar): quality metrics, agent metrics, traces, cost, and latency.
• Understanding of agent failure modes and defences: hallucination, prompt injection, and context rot.

• Solid understanding of SQL and data handling for analytics or AI workflows.
• Hands-on experience deploying LLM and agent services on Azure OpenAI or a comparable platform, including structured outputs and versioned prompts or models.
• Hands-on experience with multi-modal AI (speech, transcription, synthesis, or related domains).
• Prompt or program optimisation (e.g. DSPy) and LLM fine-tuning (LoRA, SFT).
• Experience with computer-use or voice agents.

• Strong problem-solving skills and a builder mindset.
• Curious, self-driven learner who actively explores and applies new AI frameworks and tools.
• Ability to work independently while delivering high-impact, production-ready solutions.
• Clear communicator, able to explain technical concepts, demonstrate solutions, and articulate trade-offs effectively.