At a Glance
- Tasks: Build AI-native systems for our innovative Neobank app and automate real workflows.
- Company: Join BJAK, a leading insurance platform revolutionising financial services in Southeast Asia.
- Benefits: Enjoy a hybrid work environment, competitive salary, and opportunities for professional growth.
- Other info: Fast-paced interview process with potential offers within a week for strong candidates.
- Why this job: Make a real impact by creating AI solutions that enhance user experiences and streamline operations.
- Qualifications: Hands-on experience in AI engineering, coding skills in Python or JavaScript, and a passion for fintech.
The predicted salary is between 60000 - 80000 £ per year.
About BJAK: The original mission of BJAK is we believe people deserve smarter ways to plan, save and grow their money. Started in 2019, we built the first mobile-first insurance platform, enabling insurance to be accessible online by millions in the region. Today, it is the leading insurance platform in Southeast Asia. We are expanding ways to help people in the region — this includes spending, saving, investing, exchanging, travelling, and more. Our mission is to help people get more from their money every day.
We have teams working around the world, with over 20 nationalities from our offices and remotely, who truly enjoy their work. We are looking for the most talented and driven people we can find. We want people who work for their passion, not counting hours; who love building great next-generation products, not the status quo; and who care about redefining how everyone around us can get the best financial applications, not just for an exclusive few.
The Role: We are looking for applied AI engineers to build AI-native systems for BJAK's AI Neobank app. This is not a research-only role. We need builders who can use AI to automate real workflows, improve products, reduce manual work, and make financial services easier for users and teams.
What You Will Build:
- Build AI agents, workflows, tools, and automations that solve real customer, product, and operations problems.
- Apply LLMs, retrieval, tool calling, evaluations, guardrails, and automation to practical financial workflows.
- Create AI-assisted experiences for onboarding, support, KYC, risk review, CRM, operations, document handling, and product intelligence.
- Work with product and engineering teams to identify what is worth building, then build it quickly and improve it in production.
- Design around real constraints: accuracy, hallucination risk, latency, cost, security, compliance, and user trust.
- Turn manual processes into reliable AI-enabled systems that can scale.
What We Look For:
- Hands-on engineering ability, not just AI strategy or management experience.
- Experience building AI agents, LLM apps, RAG systems, automation workflows, copilots, or AI-enabled tools.
- Strong coding ability in Python, TypeScript, JavaScript, or similar practical stack.
- Good judgement on what should be automated and what should not.
- Able to ship working systems, test quality, and improve reliability over time.
- Fintech, banking, support automation, risk, KYC, fraud, CRM, or operations automation experience is a strong plus.
This Role Is Not For:
- People who only want research projects with no production ownership.
- Candidates who treat AI as demos instead of working systems.
- Engineers who do not care about evaluation, reliability, or user impact.
- People who overclaim AI impact without evidence.
- Builders who cannot work with product, ops, and business teams.
The Bar: We are looking for engineers with real technology career highlights. A strong highlight is not just working at a famous company. A strong highlight means you personally helped build, ship, or scale a serious product, platform, or system.
Location: This role is based in the UK. This is a hybrid role. You are expected to work from our UK office at least 3 days per week.
Language: English is our main working language across global teams. Strong English communication is required.
Interview Process: Our process is designed to move fast: Online assessment or practical task, Technical interview, CEO / final round. For strong candidates, we aim to complete the process and make an offer within 1 week from the start of the interview process. Candidates who complete assessments quickly will be prioritized.
Applied AI Engineer (UK) in London employer: Bjak
BJAK is an exceptional employer that fosters a culture of innovation and collaboration, making it an ideal place for ambitious professionals looking to make a significant impact. With a focus on employee growth and development, we offer numerous opportunities for advancement in a fast-paced, high-growth environment. Join us in our mission to scale operations across multiple countries and be part of a team that values ownership and efficiency.
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We think this is how you could land Applied AI Engineer (UK) in London
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We think you need these skills to ace Applied AI Engineer (UK) in London
Some tips for your application 🫡
Show Off Your Projects:In the world of data science, your projects can speak volumes about your skills. Make sure to showcase a few key projects in your CV or portfolio, especially those that highlight your ability to work with data sets, build models, or use relevant tools like Python, R, or SQL. Don’t forget to include links to any GitHub repositories if applicable!
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Stand Out with Relevant Courses and Certifications:Although experience talks, relevant courses or certifications can be your ticket to impressing hiring managers at Bjak. Mention any standout courses you've completed that equipped you with essential skills, such as machine learning certifications or data visualisation courses. This shows your commitment to continuously developing your skills in the field!
How to prepare for a job interview at Bjak
✨Brush Up on Your Statistics
For a data science role, we need to seriously sharpen our statistics skills. Get ready to tackle technical questions on probability distributions, hypothesis testing, and regression analysis. These are often the bread and butter of data science interviews, so don't just skim over them!
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✨Get Comfortable with Python and R
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✨Prepare for Case Studies
Expect to encounter real-world case studies during the interview. We might be asked how we’d approach a data problem or analyse a dataset to extract insights. It's essential to think out loud and demonstrate our problem-solving process so that the interviewer can see our logical thinking in action.
