Dabster
Artificial Intelligence Engineer
City of Edinburgh
Posted about 22 hours ago
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Role Purpose
We are seeking a highly motivated AI Engineer to design, build and deploy scalable AI solutions across Business Transaction Banking.
The successful candidate will combine strong software engineering skills with practical expertise in AI frameworks, cloud-native platforms, data integration, and modern AI engineering practices.
The role will contribute to initiatives including:
- Generative AI and Large Language Model solutions
- Agentic AI systems and multi-agent workflows
- Retrieval Augmented Generation, embeddings, and vector search
- Conversational AI agents and knowledge management platforms
- Intelligent document processing and workflow automation
- Machine learning products and AI-powered decision support
Key Responsibilities
AI Solution Development
- Design, develop, and deploy AI-powered applications and services.
- Build scalable agentic AI solutions using modern orchestration frameworks.
- Develop RAG pipelines using vector embeddings, semantic search, and knowledge retrieval patterns.
- Implement conversational AI assistants and intelligent workflow automation.
- Integrate AI models with enterprise systems, APIs, and data platforms.
- Develop prompt engineering, evaluation, and guardrail frameworks.
Machine Learning and LLM Engineering
- Train, fine-tune, and optimise machine learning and generative AI models where appropriate.
- Build model evaluation and monitoring capabilities.
- Develop model serving and inference pipelines.
- Implement responsible AI controls and validation processes.
- Evaluate emerging AI technologies and recommend adoption opportunities.
Platform and Cloud Engineering
- Build and deploy solutions on Google Cloud Platform.
- Develop containerised applications using Docker and Kubernetes.
- Work with GKE, Vertex AI, BigQuery, Cloud Run, and related GCP services.
- Implement CI/CD pipelines and automated testing frameworks.
- Ensure scalability, resilience, security, and observability of production services.
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Data and Integration
- Integrate AI solutions with structured and unstructured data sources.
- Build ingestion pipelines from SharePoint, APIs, databases, and cloud storage.
- Develop vector stores and embedding pipelines.
- Work closely with Data Engineers to optimise data access patterns.
Innovation and Collaboration
- Create rapid proofs of concept and MVPs.
- Contribute to reusable AI frameworks, accelerators, and delivery patterns.
- Collaborate with Product Owners, SMEs, Architects, and Engineers.
- Present technical solutions to business and senior stakeholders.
- Mentor junior engineers and contribute to AI capability growth across the lab.
Essential Skills and Experience
Programming and Software Engineering
- Strong Python development experience.
- Good understanding of software engineering best practice.
- Experience building REST APIs and microservices.
- Knowledge of Git, version control, and peer review processes.
- API design, integration, security, and authentication patterns.
AI and Generative AI
- Experience working with Large Language Models.
- Practical experience with LangChain, LangGraph, or comparable agent frameworks.
- Prompt engineering, AI evaluation, and guardrail design.
- Understanding of embeddings, vector databases, and semantic search.
- Knowledge of RAG architecture and contextual retrieval patterns.
Cloud, DevOps, and Production Engineering
- Experience with GCP, Azure, or AWS.
- Containerisation using Docker.
- Kubernetes deployment experience.
- CI/CD pipeline implementation.
- Understanding of scalable, resilient, and observable cloud-native services.
Data Engineering
- Experience working with structured and unstructured datasets.
- SQL and data modelling knowledge.
- Data pipeline development experience.
- Understanding of BigQuery or equivalent analytics platforms.
Desirable Skills
- Vertex AI and Gemini models.
- OpenAI, Anthropic, or comparable LLM APIs.
- MLflow, MLOps practices, feature stores, or model registries.
- Databricks, Spark, Beam, or large-scale data processing.
- TensorFlow or PyTorch.
- Knowledge graphs, Document AI, OCR, or intelligent document processing.
- React or TypeScript front-end development.
- Financial Services, Payments, or Transaction Banking knowledge.
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Behavioural Competencies
- Strong problem-solving mindset and curiosity for emerging AI technologies.
- Passion for innovation, experimentation, and rapid learning.
- Excellent communication skills with technical and non-technical audiences.
- Ability to work in ambiguity and fast-moving delivery environments.
- Growth mindset, ownership, and accountability for outcomes.
- Collaborative working style across business, product, and technology teams.
Success Measures
- Deliver production-ready AI capabilities that create measurable business value.
- Build reusable AI patterns that can be adopted across BTB.
- Improve delivery productivity through automation and agentic workflows.
- Contribute to AI foundations and platform capabilities.
- Support successful deployment and operation of AI solutions in production.
Core Technology Stack
| Technology / Capability | Expected Application |
|---|---|
| Python | Primary AI engineering and orchestration language |
| LangChain / LangGraph | Agent orchestration, multi-step workflows, and RAG patterns |
| Vertex AI / Gemini | Model development, evaluation, and enterprise AI capabilities |
| BigQuery | Analytical data platform and AI context source |
| GCP | Cloud-native hosting and managed AI services |
| Docker and Kubernetes / GKE | Containerised deployment and scaling |
| FastAPI | API services for AI applications and agents |
| Vector databases / embeddings | Semantic search and knowledge retrieval |
| React / TypeScript | Front-end interfaces for AI tools where needed |
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Skills
Python
Generative AI
Large Language Models
LangChain
LangGraph
RAG
Google Cloud Platform
Docker
Kubernetes
Vertex AI
BigQuery
FastAPI
Vector Databases
Prompt Engineering
CI/CD
REST APIs