ai engineer - agentic systems
Summary
Build and deploy enterprise-grade AI systems, including LLMs and agentic workflows, using RAG, prompt engineering, and cloud-native tooling to power a tier-one financial institution’s AI capability.
As an AI Engineer, you will serve as a technical authority embedded within high-performing product and domain squads. You will transcend the boundary between data science and software engineering to design, build, evaluate, and scale robust, secure, and clear AI-enabled systems. Your daily mission will focus on taking advanced AI capabilities (including LLMs and complex agentic workflows) out of the lab and into repeatable, enterprise-grade production.
About the Role
As an AI Engineer, you will serve as a technical authority embedded within high-performing product and domain squads. You will transcend the boundary between data science and software engineering to design, build, evaluate, and scale robust, secure, and clear AI-enabled systems. Your daily mission will focus on taking advanced AI capabilities (including LLMs and complex agentic workflows) out of the lab and into repeatable, enterprise-grade production.
What's in it for you
- Enterprise Impact: Shape the technical culture, architecture, and evaluation frameworks of a tier-one financial institution's AI capability.
- Modern Stack: Work with advanced Generative AI, agent reasoning loops, RAG pipelines, and automated evaluation.
- Cooperative Culture: Partner closely with talented AI specialists, domain experts, and product managers in an environment that prioritises innovation and customer focus.
Key Responsibilites
- Agentic System Development: Implement agent reasoning loops, tool use interfaces, memory systems, context management, and orchestration architecture.
- Production-Grade Delivery: Translate sophisticated architectural designs into robust, tested, cloud code.
- Prompt & Context Engineering: Design advanced prompting strategies, retrieval pipelines (RAG), and context assembly to ensure highly reliable agent behaviour.
- Responsible AI Alignment: Ensure all AI behaviours are clear, auditable, and strictly aligned with enterprise ethical and safety standards.
- Rapid Experimentation: Test hypotheses regarding agent design, critically reviewing empirical data to inform long-term architectural pathways.
At Randstad, we are passionate about providing equal employment opportunities and embracing diversity to the benefit of all. We actively encourage applications from any background.