Senior AI Engineer

Hello. We’re Teya.

Teya was founded on a simple belief: local businesses deserve better.

They are the cafés, restaurants, salons, shops and entrepreneurs that bring character to our high streets, create jobs and keep communities moving. Yet for too long, financial services has made life harder for them - with clunky tools, poor support and complexity that gets in the way of running a business.

Teya exists to change that.

We’re building a financial platform for local businesses across Europe - one built around simple tools, thoughtful design and real human support. Our Members rely on us to help them run their business with confidence, and that responsibility shapes the way we work.

We move fast. We care about quality. We stay close to the detail. And we believe great performance and genuine hospitality should go hand in hand.

If you want to build meaningful products, solve real problems and make a genuine difference for local businesses, we’d love to hear from you

Your Mission

As an AI Engineer, you will help build and scale AI-powered products that directly improve how our customers interact with our platform and how our teams operate.

You’ll join our AI Research & Development team focused on creating intelligent systems where AI is core to the experience — including customer onboarding and due diligence, risk and fraud monitoring, pricing and customer lifetime value optimisation, customer support automation, and AI-assisted software development.

You’ll work across machine learning, generative AI, data, and software engineering to design, build, and productionise AI solutions that are reliable, scalable, measurable, and impactful. This role requires a strong engineering mindset, the ability to translate business problems into AI solutions, and the curiosity to explore how emerging AI capabilities can create real value.

As a Senior AI Engineer at Teya, you will be expected to:

- Design, develop, and deploy AI-powered features and services that solve real customer and business challenges.

- Build and integrate AI systems using modern approaches including LLMs, generative AI, machine learning models, retrieval-augmented generation (RAG), agents, and automation workflows.

- Work closely with product managers, engineers, data scientists, and domain experts to identify opportunities where AI can improve outcomes.

- Develop production-grade AI solutions with strong focus on quality, scalability, observability, security, and responsible AI practices.

- Evaluate AI models and solutions through experimentation, benchmarking, and continuous improvement.

- Build data pipelines, model integrations, APIs, and services that enable AI capabilities across products.

- Improve AI reliability through techniques such as prompt engineering, model evaluation, monitoring, guardrails, and feedback loops.

- Contribute to AI engineering standards, best practices, and reusable frameworks across teams.

- Stay current with developments in AI and identify opportunities to apply new technologies effectively.

Requirements:

- Strong software engineering experience with proficiency in one or more programming languages such as Python and Java

- Experience building and deploying production software systems.

- Hands-on experience developing AI/ML-powered applications or integrating AI models into products.

- Understanding of modern AI concepts including:

- Large Language Models (LLMs)

- Generative AI applications

- Prompt engineering

- Model evaluation and optimisation

- Embeddings and vector search

- Retrieval-Augmented Generation (RAG)

- AI agents and workflow automation

- Experience working with AI/ML tooling and frameworks such as OpenAI APIs, LangChain, LlamaIndex, Hugging Face, TensorFlow, PyTorch, or similar.

- Experience working with cloud platforms and modern engineering practices (CI/CD, APIs, monitoring, infrastructure as code).

- Strong understanding of software engineering principles including testing, maintainability, scalability, and system design.

- Ability to communicate technical concepts clearly to both technical and non-technical audiences.

Nice to have:

- Experience building AI solutions in regulated industries such as payments, fintech, banking, risk, compliance, or fraud prevention.

- Experience with AI use cases such as:

- Document intelligence and extraction

- Customer support automation

- Fraud detection and anomaly detection

- Decisioning systems

- Personalisation and recommendation systems

- Developer productivity tools

- Experience designing AI platforms, reusable AI infrastructure, or internal AI tooling.

- Knowledge of ML operations (MLOps), model lifecycle management, monitoring, and governance.

- Experience evaluating LLM applications for quality, safety, latency, and cost.

- Familiarity with data engineering concepts, analytics platforms, and experimentation frameworks.

- Contributions to AI communities, open-source projects, research, or technical writing.

We are committed to creating an inclusive environment where everyone regardless of race, ethnicity, gender identity or expression, sexual orientation, age, disability, religion, or background can thrive and do their best work. We believe that a diverse team leads to better ideas, stronger outcomes, and a more supportive workplace for all.

If you require any reasonable adjustments at any stage of the recruitment process whether for interviews, assessments, or other parts of the application—we encourage you to let us know. We are committed to ensuring that every candidate has a fair and accessible experience with us.