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- Tasks: Build and ship AI features that make technology work for users in real-world scenarios.
- Company: Join a forward-thinking AI company focused on innovative solutions.
- Benefits: Competitive salary, flexible working hours, and opportunities for professional growth.
- Other info: Dynamic team environment with a focus on collaboration and continuous improvement.
- Why this job: Be at the forefront of AI development and create impactful products that change lives.
- Qualifications: Experience in machine learning, coding skills, and problem-solving abilities.
The predicted salary is between 60000 - 80000 £ per year.
A1 is building a proactive AI system that understands context across conversations, plans actions, and carries work forward over time. As an Applied AI Engineer, you will turn model capabilities into real product behaviour. You will own problems end-to-end, from shaping model behaviour, to building the systems around it, to ensuring it performs reliably in production. This role sits at the intersection of machine learning, systems, and product, focusing on making AI actually work for users, not just in demos, but in real-world usage.
Responsibilities
- Build and ship AI features end-to-end (model system user experience)
- Design and iterate on prompts, tools, memory, and agent workflows
- Turn raw model outputs into structured, reliable, and predictable behaviours
- Debug issues across the full stack (model, orchestration, infra, UX)
- Optimize for latency, cost, and production reliability
- Develop lightweight evaluation frameworks to measure real-world performance
- Work closely with product and engineering to translate ambiguous problems into working systems
Tech Stack
- Python
- PyTorch/JAX
- LLMs (OpenAI-style APIs, LLaMA, Qwen, etc.)
- Inference/serving (e.g., vLLM)
- Vector DB
Ideal Experience
- Strong foundation in machine learning and modern neural network architectures
- Hands-on experience with training, fine-tuning, or deploying ML models
- Ability to write clean, production-quality code
- Comfort working across abstraction layers (model, infra, product)
- Strong problem-solving skills in ambiguous, fast-moving environments
- Bias toward shipping, iteration, and continuous improvement
Outcomes
- ML models in production meet expected accuracy, latency, and reliability targets
- Production issues are identified quickly, debugged effectively, and root causes addressed
- Data pipelines, training loops, and inference systems are robust, reproducible, and maintainable
- Collaboration with engineers, product, and research teams delivers reliable ML-powered features
- Iterations on models and systems are driven by real-world signals and measurable improvements
Applied AI Engineer employer: Bjak Sdn Bhd
Contact Detail:
Bjak Sdn Bhd Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Applied AI Engineer
✨Tip Number 1
Network like a pro! Reach out to folks in the AI and machine learning space on LinkedIn or at meetups. We all know that sometimes it’s not just what you know, but who you know that can help you land that Applied AI Engineer role.
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your projects, especially those involving Python, PyTorch, or LLMs. We want to see how you turn model capabilities into real product behaviour, so make sure to highlight your hands-on experience.
✨Tip Number 3
Prepare for technical interviews by brushing up on your problem-solving skills. We recommend practicing coding challenges and discussing your thought process out loud. This will help you demonstrate your ability to tackle ambiguous problems effectively.
✨Tip Number 4
Don’t forget to apply through our website! It’s the best way to ensure your application gets seen by the right people. Plus, we love seeing candidates who are proactive about their job search!
We think you need these skills to ace Applied AI Engineer
Some tips for your application 🫡
Tailor Your CV: Make sure your CV reflects the skills and experiences that align with the Applied AI Engineer role. Highlight your hands-on experience with machine learning, Python, and any relevant projects you've worked on. We want to see how you can turn model capabilities into real product behaviour!
Craft a Compelling Cover Letter: Your cover letter is your chance to shine! Use it to explain why you're passionate about AI and how your background makes you a great fit for our team. Don’t forget to mention specific examples of how you've tackled ambiguous problems in the past – we love a good story!
Showcase Your Problem-Solving Skills: In your application, be sure to highlight your problem-solving skills, especially in fast-moving environments. Share examples of how you've debugged issues across different layers or optimised systems for performance. We’re looking for someone who can think on their feet and deliver results!
Apply Through Our Website: We encourage you to apply directly through our website. It’s the best way for us to receive your application and ensures you don’t miss out on any important updates. Plus, it shows us you’re keen to join the StudySmarter family!
How to prepare for a job interview at Bjak Sdn Bhd
✨Know Your Tech Stack
Familiarise yourself with the tech stack mentioned in the job description, especially Python, PyTorch/JAX, and LLMs. Be ready to discuss your hands-on experience with these technologies and how you've used them to build or optimise AI features.
✨Showcase Problem-Solving Skills
Prepare examples that highlight your problem-solving abilities in ambiguous situations. Think of times when you debugged issues across the full stack or optimised for latency and reliability. This will demonstrate your capability to handle the fast-paced environment they’re looking for.
✨Understand the User Experience
Since this role focuses on making AI work for users, be prepared to discuss how you would design user experiences around AI features. Think about how you can turn raw model outputs into structured behaviours that are reliable and predictable for end-users.
✨Emphasise Collaboration
Highlight your experience working closely with product and engineering teams. Share specific instances where collaboration led to successful outcomes, especially in translating ambiguous problems into effective solutions. This shows you can work well in a team-oriented environment.