[Hiring] Senior AI Engineer @Gramian Consulting Group

Role Description

Our client is a Big4 Consultancy group that works with leading financial institutions on AI-driven transformation, automation, advanced analytics, and financial crime prevention. Their work spans intelligent fraud detection, AML/KYC modernization, autonomous workflows, enterprise AI platforms, and the secure industrialization of AI in highly regulated environments.

We are looking for Senior AI Engineers to design, build, and deploy production-grade autonomous agents, multi-agent systems, and LLM-powered enterprise applications. The role will work across multiple delivery squads and focus on:

• Reusable architecture
• Agent orchestration
• Tool integration
• Cloud deployment
• Evaluation of long-running AI workflows

CONTRACT: Contractor assignment, expected October 2026 – July 2027, with potential extension

COMMITMENT: Full-time

LOCATIONS: Europe-based, preferably CEE; remote, with potential future hybrid work in Prague

PROCESS: Initial qualification followed by technical and client interviews

NOTES: Fluent English is required. Strong AWS experience is highly preferred.

Qualifications

• 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

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 the 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