Key Responsibilities
- Design, develop and deploy scalable AI/GenAI applications using Python.
- Build LLM, RAG, AI Agent and multi-agent solutions, including embeddings, vector search and semantic retrieval.
- Develop robust backend services and APIs using FastAPI, Flask or Django.
- Integrate AI solutions with enterprise applications, databases and cloud services.
- Implement Docker, Kubernetes and CI/CD practices for reliable production deployments.
- Apply strong engineering practices around security, scalability, performance, evaluation and observability.
- Collaborate with engineering, product and business teams to deliver enterprise AI solutions.
Key Skills
- Python & Backend Engineering – Strong Python development with FastAPI, Flask or Django.
- GenAI & LLMs – Hands-on experience with LLMs, Prompt Engineering, RAG, Embeddings, Vector Databases and Semantic Search.
- Agentic AI – Experience with AI Agents and frameworks such as LangChain, LangGraph, LlamaIndex or Semantic Kernel.
- Cloud & DevOps – Azure/AWS/GCP, Docker, Kubernetes, CI/CD, Git and GitHub Actions.
- Data & Architecture – SQL/NoSQL, vector stores, APIs, software architecture, security, scalability and performance optimisation.
Highly Desirable
- Production experience building Agentic AI and multi-agent workflows.
- AI governance, Responsible AI, evaluation frameworks and model observability.
- Banking, financial services, cards, payments, lending or other regulated-industry experience.
- Experience with GitHub Copilot and AI-assisted software development.
Please drop you CV [email protected]