Senior AI Engineer for Autonomous Agents & LLM-Powered Enterprise Applications

About the Role

Our client, a Big4 Consultancy group, specializes in AI-driven transformation, automation, advanced analytics, and financial crime prevention for leading financial institutions. Their work includes intelligent fraud detection, AML/KYC modernization, autonomous workflows, enterprise AI platforms, and secure AI industrialization in highly regulated environments.

We are seeking Senior AI Engineers to design, build, and deploy production-grade autonomous agents, multi-agent systems, and LLM-powered enterprise applications. The role spans multiple delivery squads, focusing on reusable architecture, agent orchestration, tool integration, cloud deployment, and evaluation of long-running AI workflows.

Responsibilities

• Architect and build production-grade autonomous AI agents and multi-agent orchestration frameworks.
• Develop reusable agent patterns and technical standards across multiple engineering squads.
• Integrate LLMs with APIs, databases, proprietary tools, and enterprise systems.
• Implement reliable tool-calling and structured-output workflows.
• Design and optimize prompt strategies, context management, memory, and agent state.
• Build stable, long-running agent workflows with appropriate error handling and recovery mechanisms.
• Implement monitoring, logging, tracing, and evaluation frameworks for agent behavior and model outputs.
• Support deployment of AI systems on public cloud infrastructure, primarily AWS.
• Apply MLOps/AIOps practices across versioning, testing, monitoring, and evaluation.
• Collaborate with engineering, data, cloud, and business teams to deliver secure and scalable AI solutions.

Requirements

• 5–10 years of professional software, data, or AI engineering experience.
• Strong hands-on development experience with Python.
• Proven experience designing and integrating LLM-powered or agentic AI applications.
• Experience with agent orchestration frameworks such as LangChain, AutoGen, CrewAI, or comparable technologies.
• Strong experience with enterprise AI integration patterns including MCP, A2A, structured outputs, tool calling, or skills-based architectures.
• Professional experience designing and deploying AI solutions on public cloud platforms, preferably AWS.
• Experience with MLOps/AIOps practices, including versioning, testing, monitoring, and evaluations.
• Fluent professional English.