AI Engineer

Light IT Global

Charing Cross, United Kingdom

19 days ago

Role details

Contract type

Permanent contract

Employment type

Full-time (> 32 hours)

Working hours

Regular working hours

Languages

English

Job location

Charing Cross, United Kingdom

Tech stack

Artificial Intelligence

Airflow

Amazon Web Services (AWS)

Azure

Cloud Computing

Python

Prometheus

Next.js

Data Streaming

TypeScript

React

Large Language Models

Grafana

Caching

FastAPI

Build Management

low-code

Front End Software Development

Docker

Job description

Design & build LLM applications: RAG over client KBs, multi-tool/agent graphs, prompt strategies, and voice UX (STT/TTS).
Implement retrieval & data flows: ingestion, chunking, metadata/embeddings, hybrid retrieval, re-ranking, caching.
Own evaluation & quality: latency/cost budgets, A/Bs.
Automate with n8n (or similar): connect services, data, and back-office workflows; expose reliable admin ops for non-engineers.
Ship pipelines: Airflow for orchestration; dbt for transformations/semantic layer where relevant.
Productionize: tracing/observability, feature flags, safe rollouts, secrets & config hygiene.
Collaborate with clients: discover scope, explain trade-offs, demo progress; optional presales spikes/estimates.

Nice to have:

Vector DBs: pgvector, Qdrant, Pinecone, Azure Cognitive Search (vector); rerankers (BGE/monoT5/Cohere/ColBERT).
Voice: Azure Speech, Whisper; VAD/barge-in, turn-taking logic.
Data stack: dbt, Airflow, event streams; data contracts.
Workflow: n8n design for non-tech ops; governance of low-code automations.
Frontend for AI UX (React/Next.js, TypeScript).
Compliance awareness for EdTech (FERPA/COPPA) and Healthcare (HIPAA/GDPR).

Requirements

Do you have experience in TypeScript?, Do you have a Master's degree?, * Strong Python (typing, tests, packaging) and web/service basics (FastAPI, Docker).

Hands-on building LLM apps with one or more: LangChain, LangGraph, AutoGen, Dify (or equivalents).RAG end-to-end experience: corpus prep embeddings retrieval re-rank context assembly
measurable gains.
Evaluation mindset: design offline eval sets and release gates; reason about faithfulness vs relevancy; control p95 latency & $/session.
Cloud proficiency in AWS or Azure (using at least one in production).
Comfort with observability (OpenTelemetry/LangSmith/Prometheus/Grafana or similar).
English B2+; clear written & verbal client communication.