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 NumPy, Pandas, SQL, and use of Git/GitHub 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: LangChain, LlamaIndex – 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: Model versioning, monitoring, logging
- Bias Detection & Mitigation
- Content Filtering & Moderation
- Explainability & Transparency
- LLM Safety & Guardrails: Hallucination mitigation, prompt validation, safety layers
- Azure Cloud Experience
- Cross-functional Collaboration: Working with software engineers, DevOps, 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
- 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