AI Engineer -- Agentic AI Systems
LEC AI
Central London (Knightsbridge) | Full-Time | 5 Days On-Site
About LEC AI
LEC AI is the artificial intelligence division of London Export Corporation, a British trading group established over 73 years ago operating across multiple sectors and international markets.
We are building production AI systems that serve as the operational brain for the organisation -- multi-agent architectures that handle memory, reasoning, task management, document intelligence and cross-departmental coordination in real time.
This is not a research lab. We ship production systems, iterate fast, and operate with the intensity of a startup backed by the resources and commercial reach of an established international group.
The Role
We are looking for an AI Engineer with 2-5 years of engineering experience who can build end-to-end: from designing agentic architectures to deploying them on cloud infrastructure, from writing RAG pipelines to wiring up tool-use protocols, from prompt engineering to system design.
You will work directly with senior leadership and the founding technical team. This is a small, high-trust environment where your work ships to production the same week and is used daily by the people running the business.
This role is for someone who thrives in ambiguity, moves fast when requirements change daily, and would rather build something that works today than plan something perfect for next quarter.
You will be a core builder -- not a ticket-taker. You will own entire systems, make architectural decisions, and ship directly to production. If you need someone to tell you what to do every morning, this is not the role for you.
Key Responsibilities
Agentic AI Systems
- Design, build and maintain multi-agent architectures with tool calling, memory, reasoning loops and inter-agent communication
- Implement agentic loops (prompt assembly, LLM call, tool execution, iteration) with circuit breakers and observability
- Build and extend tool registries, function calling schemas, and execution pipelines
- Work with tool-use protocols like MCP for interoperability across agents and services
RAG & Memory Systems
- Build and optimise retrieval-augmented generation pipelines across structured and unstructured data
- Implement hybrid memory stacks using things like vector databases, knowledge graphs, session caching and object storage
- Design embedding pipelines, chunking strategies, and semantic search with re-ranking
- Build document ingestion systems that process PDFs, spreadsheets and unstructured documents into searchable knowledge
Real-Time & Multi-Modal Systems
- Build streaming data pipelines, live communication channels and event-driven architectures
- Work with audio processing, speech-to-text and multi-modal data pipelines
- Design systems that handle both batch and real-time workloads in production
Software Engineering & System Design
- Write production backend and frontend code (like Python, JavaScript/TypeScript, React)
- Design APIs, data models, and system architectures that scale
- Understand trade-offs between simplicity and abstraction -- build what is needed, not what is theoretically elegant
- Write code that other people can read, debug and extend
- Implement production security practices -- access control, data isolation, PII handling and authentication flows
Deployment & Infrastructure
- Deploy and manage containerised services on cloud infrastructure
- Set up CI/CD, monitoring, logging and observability
- Manage databases and storage in production (like Postgres, graph databases, caches, S3-compatible stores)
- Handle DNS, reverse proxies, SSL, tunnels -- the full stack of getting things live and keeping them live
Rapid Prototyping & AI-Assisted Development
- Use AI-assisted development tools effectively to move at speed
- Prototype new capabilities quickly, validate with real usage, then harden
- Comfortable building MVPs in hours, not weeks
Candidate Profile
The ideal candidate has 2-5 years of engineering experience and is a builder who ships. You combine deep AI/LLM knowledge with real software engineering discipline and the operational grit to deploy and maintain production systems.
Must have:
- 2-5 years of software engineering experience, with meaningful time spent on AI/LLM systems
- Hands-on experience building agentic AI systems (not just calling an API and returning the response)
- Strong understanding of RAG architectures, vector databases and semantic search
- Production software engineering skills -- you have deployed and maintained real systems
- Experience with containerisation, Linux, and cloud deployment
- Comfort with rapidly changing requirements and ambiguity
- A bias toward action -- you try things, break things, fix things, ship things
Strong signals:
- You have built multi-agent systems or AI orchestration pipelines
- You understand tool-calling protocols, MCP, and LLM integration patterns