AI Engineer

Certain Advantage are recruiting on behalf of our Trading client for an AI Engineer on a contract basis for 6-12 months initially in London. This will require some onsite days in Central London during the week.

We are seeking Engineers skilled in python with a strong focus on GenAI AI and LLMs to lead the integration of cutting-edge language technologies into real-world applications.

If you’re someone passionate about building scalable, responsible, and high-impact GenAI solutions then this could be for you!

We’re looking for Engineers offering competent core technical skills in Python Programming, Data Handling with Num Py, Pandas, SQL, and use of Git/Git Hub for version control.

Any experience with these GenAI Use Cases would be relevant and desirable;
Chatbots, copilots, document summarisation, Q&A, content generation.

To help make your application as relevant as possible, please ensure your CV demonstrates any prior experience you have relating to the below;  

System Integration & Deployment

Model Deployment:
Flask, FastAPI, MLflow
Model Serving:
Triton Inference Server, Hugging Face Inference Endpoints
API Integration:
OpenAI, Anthropic, Cohere, Mistral APIs
LLM Frameworks:
Lang Chain, Llama Index – for building LLM-powered applications
Vector Databases: FAISS, Weaviate, Pinecone, Qdrant (Nice-to-Have)
Retrieval-Augmented Generation (RAG):
Experience building hybrid systems combining LLMs with enterprise data

MLOps & Infrastructure

MLOps:
Model versioning, monitoring, logging
Bias Detection & Mitigation
Content Filtering & Moderation
Explainability & Transparency
LLM Safety & Guardrails:
Hallucination mitigation, prompt validation, safety layers
Azure Cloud Experience
Collaboration & Delivery

Cross-functional Collaboration:

Working with software engineers, Dev Ops, and product teams
Rapid Prototyping:
Building and deploying MVPs
Understanding of ML & LLM Techniques:
To support integration, scaling, and responsible deployment
Prompt Engineering:
Designing and optimising prompts for LLMs across use cases
Model Evaluation & Monitoring

Evaluation Metrics:
Perplexity, relevance, response quality, user satisfaction
Monitoring in Production:
Drift detection, performance degradation, logging outputs
Evaluation Pipelines:
Automating metric tracking via MLflow or custom dashboards
A/B Testing:
Experience evaluating GenAI features in production environments
Does this sound like your next career move? Apply today!

Working with Certain Advantage

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