Senior AI Engineer, Solutions

At YTL AI Labs, we build sovereign AI models that perform on par with the world’s best—while staying grounded in local needs, values, and context. Our flagship model, Ilmu, is designed to be culturally aware, contextually intelligent, and fluent in Bahasa Melayu, delivering cutting‑edge solutions that empower Malaysian businesses with intelligence that truly understands the market and the people they serve.

As pioneers of sovereign AI, we believe every nation should have the power to shape its own intelligence—guided by its people, priorities, and principles.

About the role

This is a senior, hands‑on AI engineering role. We are not looking for a researcher who writes papers, nor a prompt tinkerer who treats LLMs as black boxes. We are looking for an engineer who designs and ships AI systems end to end, owns the data and evaluation alongside the model layer, and is embedded in the team from the first day of a project.

You will be involved from requirements gathering, help shape what “good” looks like for each AI feature, and build production‑grade pipelines, agents, and RAG systems in parallel with the rest of the product. AI here is treated as part of engineering, not a science project. Your day‑to‑day work is primarily coding: building retrieval pipelines, agent orchestration, evaluation harnesses, and the supporting infrastructure that keeps them reliable.

We release fast. Some products, particularly ILMUchat Enterprise, ship daily. That cadence is only possible because AI features are built with the same rigour as the rest of the stack, with automated evals, monitoring, and clear regression signals.

What you will do

• Participate in requirements gathering with clients and the engineering team, and help define what success looks like for each AI feature, including the evaluation criteria.
• Determine the AI approach per project: choice of model, retrieval strategy, agent design, fine‑tuning where it earns its place, and the right balance of cost, latency, and quality.
• Build retrieval‑augmented generation pipelines end to end, including ingestion, chunking, embeddings, vector stores, retrieval, and re‑ranking.
• Design and ship agentic workflows and tool‑using systems, with proper guardrails, fallbacks, and observability.
• Own the evaluation layer: build offline eval sets, online evals, and regression tests so model and prompt changes can ship with confidence.
• Integrate AI components into production back‑ends and front‑ends, working across the stack alongside the rest of the team.
• Liaise closely with engineers and QA on user stories, acceptance criteria, and test cases for AI behaviour.
• Produce clear progress and quality signals that leadership uses to track delivery status and model performance over time.

What we are looking for

• 5+ years experience in software or AI engineering roles
• Strong hands‑on experience building production LLM applications, not just demos or notebooks.
• High technical proficiency in Python, and comfort writing the back‑end code that surrounds AI components; this role is engineering, not manual prompt tuning.
• Solid experience with RAG: embeddings, vector databases, retrieval quality, chunking strategies, and re‑ranking.
• Experience designing agents and tool‑using systems, including handling failure modes, loops, and cost control.
• Experience defining and running evaluations for LLM features, both offline and in production.
• Familiarity with at least one major model provider API (OpenAI, Anthropic, Google, or similar) and with running open‑weight models where appropriate.
• Ability to work directly with clients and engineers, contributing to requirements and shaping what gets built.
• Comfortable in a fast, lean environment with daily releases and client‑driven timelines.