Up to £450/day Outside IR35
London
2 days per week in Office
We are seeking a highly skilled AI Engineer with deep expertise in Agentic AI, Large Language Models, NLP, GenAI pipelines, cloud ML platforms, and vector-based retrieval systems.
This is an opportunity to join an advanced AI team building next‑generation intelligent systems, multi‑agent applications, and high‑scale GenAI microservices. You will design, deploy, an...
Up to £450/day Outside IR35
London
2 days per week in Office
We are seeking a highly skilled AI Engineer with deep expertise in Agentic AI, Large Language Models, NLP, GenAI pipelines, cloud ML platforms, and vector-based retrieval systems.
This is an opportunity to join an advanced AI team building next‑generation intelligent systems, multi‑agent applications, and high‑scale GenAI microservices. You will design, deploy, and optimise production-grade AI/ML systems powering millions of customer interactions.
You will work across Python, cloud-native architectures, vector search, RAG frameworks, orchestration engines, and multi-agent systems, shaping AI capabilities that transform how organisations interact, automate, and understand their customers.
Key Responsibilities
AI / LLM / Agentic Engineering
- Design, build, and optimise agentic AI systems using frameworks such as LangChain, LangGraph, Vertex AI Agent Builder, Bedrock Agents, AgentKit, CrewAI, and custom orchestration.
- Build LLM-powered applications using models including GPT‑4o/5, Llama3, Claude, Gemini 2.5 Pro, Bard, and enterprise-grade LLM deployments.
- Implement RAG and CAG architectures using Pinecone, OpenSearch, Google GenAI Search, and custom vector stores.
- Engineer domain‑tuned embeddings using ADA‑002, Gecko, Word2Vec, BERT, Sentence Encoder, and topic modelling.
AI/ML Pipelines & MLOps
- Develop scalable AI/ML microservices using Docker, Kubernetes (EKS/GKE), and CI/CD‑driven automation.
- Build and enhance pipelines for model evaluation, bias/drift detection, real‑time inference, and monitoring.
- Optimise inference latency for high‑volume, near-real-time applications such as transcript and behavioural analysis.
NLP & Applied Machine Learning
- Apply text clustering, N‑gram analytics, sentiment modelling, intent classification, and summarisation for insight extraction.
- Refine conversational intent taxonomies and behavioural models for more accurate AI assistant interactions.
Data Engineering & Cloud Integration
- Use cloud services including SageMaker, Azure ML Studio, Vertex AI for training, deployment, and monitoring.
- Manage datasets using GCP Cloud Storage and implement secure, compliant data workflows.
AI Governance & Quality Assurance
- Establish guardrails, safety layers, automated evaluation frameworks, and prompt governance patterns.
- Ensure all AI systems meet stringent data governance, privacy, and financial‑sector compliance requirements.
Technical Skills
Languages & Development
- Python, Java, SQL, Shell Scripting, Node.js, Streamlit
- IDE experience: PyCharm, VS Code, JupyterLab, Eclipse, Notepad++, Sagemaker Studio, Azure ML Studio, Vertex AI Workbench
Python Libraries
- NumPy, Pandas, Matplotlib, Scikit-learn, TensorFlow, Keras, PyTorch, PySpark, SpaCy, SciPy, NLTK, Statsmodels, Boto3, AzureSDK
NLP & LLMs
- BERT, Word2Vec, Universal Sentence Encoder, NLTK, embeddings, fuzzy matching, topic modelling
- LLM experience: GPT‑3.5/4o/5, Llama2/3, Claude, Gemini, Bedrock models, SQuAD fine‑tuning, custom RAG agents
AI Search & Vector Innovations
- Pinecone, OpenSearch, LangChain/LangGraph, LangIndex, Vertex AI Search, Vector DBs, RAG pipelines
What We're Looking For
- Proven experience developing production-grade LLM, GenAI, NLP, or agent-based AI systems.
- Strong engineering foundation across Python, cloud platforms, APIs, and vector search.
- Experience with complex multi-agent AI orchestration.
- Ability to deliver high‑scale, low‑latency AI solutions in demanding environments.
- Strong collaboration, architectural thinking, and a passion for cutting‑edge AI innovation.
