Artificial Intelligence Engineer

Oscar Associates (UK) Ltd
Charing Cross, United Kingdom
9 days ago

Role details

Contract type
Temporary contract
Employment type
Full-time (> 32 hours)
Working hours
Regular working hours
Languages
English
Compensation
£ 130K

Job location

Charing Cross, United Kingdom

Tech stack

Artificial Intelligence
Amazon Web Services (AWS)
Azure
Cloud Computing
Databases
Continuous Integration
Python
Sentiment Analysis
Large Language Models
Multi-Agent Systems
Generative AI
Build Management
Machine Learning Operations

Job description

  • Design and build Generative AI solutions using LLMs, RAG architectures and agent-based workflows
  • Develop and orchestrate multi-agent AI systems (e.g. supervisor and specialist agents)
  • Implement retrieval pipelines using vector databases to ground LLM responses
  • Build and optimise NLP pipelines for sentiment analysis, intent detection and document understanding
  • Deploy AI/ML models into production using cloud-native architectures
  • Implement MLOps best practices including CI/CD, monitoring, observability and evaluation
  • Work closely with engineering, product and data teams to translate business problems into AI solutions
  • Ensure solutions are scalable, interpretable and production-ready

Requirements

  • Strong commercial experience as an AI Engineer / ML Engineer / Applied AI Scientist
  • Advanced Python experience for ML and AI development
  • Hands-on experience with LLMs & Generative AI, including:
  • Retrieval-Augmented Generation (RAG)
  • Prompt & context engineering
  • Agentic / multi-agent architectures
  • Experience with frameworks such as LangChain, LangGraph, LangSmith or similar
  • Solid NLP background (tokenisation, embeddings, transformers, BERT-style models, sentiment analysis)
  • Experience integrating vector databases (e.g. Azure AI Search, OpenSearch, Pinecone, Weaviate)
  • Cloud experience on Azure, AWS or GCP
  • Familiarity with MLflow, model evaluation frameworks, observability & monitoring
  • Strong understanding of deploying AI systems into real-world production environments

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