AI Engineer

Sambla Group is a fintech company with a clear goal – to be your trusted financial partner. We’ve grown into one of the fastest-growing fintech companies, bridging the gap between borrowers and lenders, thanks to the 500+ amazing people who work here. In the summer of 2026, we were granted authorisation as a credit market company, marking an important milestone in our continued journey to expand into own lending and opening an exciting new chapter for Sambla Group.

We’re hiring an experienced AI engineer to help us build the next generation of AI financial products and help us scale AI across Sambla Group.

You’ll sit at the intersection of Product & Tech and Data Science, working closely with our AI PM, DS/ML, MLOps, security and backend teams. Your focus is to turn AI ideas into robust, safe and value-driving products – and to put in place the patterns, tooling and guardrails that let other teams do the same.

Help drive forward our GenAI / LLM foundations

• You design and evolve the architecture for LLM‑based products (chatbots, copilots, automation, retrieval) on AWS, integrating into our Python/TypeScript backend landscape. You define patterns for RAG, prompts, agents and evaluation that other teams can reuse.

Make AI safe, reliable and cost‑efficient

• You work with Security and Legal on data privacy, access control and provider/model choices. You build protections against prompt injection and unsafe outputs, integrate AI services with our monitoring/alerting, and optimize prompts, caching and model selection to balance quality, latency and cost.

Build and evolve the internal AI platform

• You create and maintain SDKs, libraries, templates and reference services so all Sambla Group product teams can ship GenAI features faster and more consistently. Together with MLOps you align ML and LLM pipelines instead of letting them drift apart.

Ship and own key AI features

• Together with data science, product & tech you take end‑to‑end technical ownership of a few high‑impact AI initiatives (for example an in‑app chatbot or internal copilots) – from spike and design to implementation, rollout and continuous improvement. You turn ideas into clear technical plans and de‑risk new approaches through prototypes.

• 5+ years in software and/or ML engineering, including at least 2–3 years working with LLM/GenAI systems in production.
• Proven track record of shipping AI/ML/LLM‑powered features to end users and operating them in production (monitoring, debugging, iterating).
• Strong backend engineering skills in TypeScript and/or Python.
• Comfortable with cloud‑native architecture on AWS (containers/serverless, networking, IAM, logging/monitoring), ideally including Bedrock.
• Deep practical experience with LLM ecosystems:
• Hosted and/or self‑hosted models.
• RAG pipelines, prompt patterns and guardrails.
• Evaluating and improving LLM outputs.
• Familiar with MLOps/LLMOps concepts and tooling: CI/CD, model and prompt versioning, experiment tracking, monitoring (latency, error rates, quality metrics) and data pipelines.
• Strong system‑design skills for services/APIs, data flows and integrations in a microservice/product architecture.
• Comfortable operating as an experienced individual contributor: setting technical direction, influencing without line authority and mentoring others.
• Used to working closely with product managers, data scientists and engineers, and explaining complex trade‑offs in a clear, structured way.
• Driven by curiosity and ownership, always looking for where AI and ML can make a real difference for our customers and across the business.
• Experience from an AI‑mature, product‑led company using AI/LLMs at scale.
• Exposure to data/knowledge‑management for LLMs (enterprise search, ingestion pipelines, knowledge graphs, ontology/tagging).
• Data science / ML background strong enough to discuss when to use classic ML vs LLMs vs rules.
• Experience with security and compliance for AI in regulated or semi‑regulated environments (e.g. financial services).

We might be a little different from most companies. Instead of defining personal values, we’ve built a shared way of working - a rhythm that guides how we collaborate, support our customers, and move forward together. We call it Drive & Jive - a shared rhythm that creates momentum through direction, collaboration, and human connection.

As part of our recruitment process, we always carry out a background check before extending a job offer.