Full Stack AI Engineer

Role Purpose

The AI Engineer (Full Stack) is responsible for designing, building, and deploying scalable AI‑enabled use cases. The role combines backend engineering, data engineering, and Generative AI capabilities to deliver production‑grade solutions that integrate seamlessly into enterprise workflows. The engineer contributes to the development of intelligent capability patterns, including retrieval‑based systems, AI agents, and full‑stack applications, leveraging a combination of modern cloud platforms, APIs, and AI tooling.

This is a hands‑on engineering role focused on building real, deployable AI solutions rather than experimentation or isolated prototypes. The engineer operates across the full lifecycle of AI delivery, from data preparation and knowledge engineering to LLM orchestration and application development. The role requires the ability to work across multiple platforms, including cloud‑native environments with an AWS focus and some Azure exposure, and integrate AI capabilities into secure, scalable, and reusable systems.

The engineer is expected to contribute to both rapid delivery and the establishment of repeatable engineering patterns.

• Full stack AI solution development
  • Design and develop end‑to‑end AI applications, including backend services, APIs, and user‑facing components.
  • Build production‑grade solutions that expose AI capabilities through web applications, services, or agents/bots.
  • Support authentication, integration, and deployment of AI‑enabled applications into enterprise environments.
• AI and LLM engineering
  • Design and implement LLM‑based workflows, including prompt engineering, tool calling, and structured outputs.
  • Build and integrate AI agents with defined capabilities, tools, and execution logic.
  • Contribute to multi‑step reasoning flows and agent‑based architectures where required.
• Pattern recognition
  • Identify where similar use‑cases can be adopted in more than one area.
• Retrieval and knowledge engineering
  • Develop retrieval‑based AI solutions, including vector search and knowledge grounding patterns.
  • Transform structured and unstructured data into AI‑ready formats, including embeddings and indexed datasets.
  • Ensure AI outputs are grounded, explainable, and aligned to defined controls and quality standards.
• Data engineering and integration
  • Build data pipelines that enable AI systems to interact with enterprise data sources.
  • Integrate AI capabilities into core systems using APIs and microservices.
  • Support patterns such as text‑to‑SQL, curated data views, and AI‑driven access to structured data.
• Cloud and platform engineering
  • Develop and deploy AI solutions on cloud platforms, with a focus on AWS (e.g., Lambda, S3, API Gateway, Bedrock, Sage Maker) and exposure to Azure‑based services.
  • Integrate AI tooling and services into cloud‑native architectures using secure and scalable design patterns.
  • Apply modern Dev Ops practices, including CI/CD, environment management, and automated deployment pipelines.
• Engineering standards and delivery
  • Write high‑quality, production‑grade code in Python using object‑oriented and modular design principles.
  • Contribute to architecture discussions and support the evolution of reusable AI engineering patterns.
  • Ensure solutions are maintainable, testable, and aligned to enterprise engineering standards.

• Production‑grade Python engineering – ability to build and operate reliable backend services and orchestration layers, object‑oriented programming, utilizing CI/CD pipelines, etc.
• AI retrieval & grounding engineering – ability to design retrieval pipelines that control hallucination and ensure AI model outputs within CIB Model Risk frameworks and appetite, including explainability and auditability.
• LLM orchestration & tool integration – including engineering prompt flows, function/tool calling, and structured outputs as part of systems, not just ad‑hoc prompting.
• Experience with open‑standard agent integration (MCP) and modeling of Model Context Protocol or equivalent open standards to integrate AI safely with enterprise systems, enterprise API & microservices design.
• Hands‑on experience working with LLM platforms such as OpenAI,…