AI Engineer - GenAI & Agentic Systems

Flat Gigs is hiring on behalf of a confidential enterprise banking client undergoing a large-scale Trade Transformation initiative powered by Generative AI.

We are looking for high-impact engineers who can design and deploy enterprise-grade Agentic AI systems integrating LLMs with secure structured private data environments.

This is not a research role. This is production engineering inside regulated enterprise systems.

Immediate joiners are strongly preferred.

The AI Engineer will design, build, and deploy advanced multi-agent AI systems embedded within enterprise trade platforms.

The role requires strong full-stack engineering capability combined with deep expertise in:

• Agentic AI architectures
• LLM orchestration
• Secure enterprise integrations
• Prompt engineering
• Vector search systems

• Design and implement end-to-end multi-agent workflows
• Connect LLMs with structured private enterprise data
• Build modular AI agents with reasoning and tool orchestration
• Ensure explainability and traceability in regulated environments

• Design and optimise advanced prompt frameworks
• Build guardrails and fallback logic for enterprise stability
• Implement evaluation frameworks to measure hallucination and output consistency

• Develop scalable backend services using Node.js (Express / NestJS)
• Build secure frontend applications using React + Type Script
• Develop RESTful APIs exposing AI workflows
• Embed AI modules into enterprise trade systems

• Integrate GenAI services using secure API frameworks
• Implement role-based access control
• Ensure compliance and sensitive data handling standards

• Build using Lang Chain / Lang Graph for multi-agent orchestration
• Implement memory layers and workflow graphs
• Work with vector databases (Redis preferred)
• Design and optimise RAG (Retrieval-Augmented Generation) pipelines

• 5+ years of full-stack software engineering experience
• Strong hands-on experience with Node.js and React
• Proven experience building LLM-powered production systems
• Experience designing multi-agent AI workflows
• Strong understanding of vector databases (Redis preferred)
• Experience building secure AI systems in regulated domains

• Trade Finance / Treasury / Capital Markets exposure
• Model evaluation frameworks or LLMOps exposure
• Containerisation (Docker / Kubernetes)
• CI/CD pipeline integration
• AI governance and data compliance understanding

• Builder mindset
• Enterprise-grade engineering discipline
• Comfortable working in high-security environments
• Able to ship production AI, not just prototypes