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Applied AI EngineerBJAK · Germany
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BJAK

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Applied AI Engineer

🗓️ Posted 2026-10-07 Germany Fulltime Hybrid ICT
💰 Salary Not shared 📊 Market 5,000 EUR–8,000 EUR/mo

About the Company

BJAK Sdn Bhd (Company No. 201901030483 (1339813-K) Our mission is to develop technology based solutions to improve financial inclusion. We develop new & innovative platforms & services globally. For example, we are the first platform to simplify and digitise comprehensive life and medical insurance, supported by AI agent. BJAK is the largest insurance platform in Southeast Asia. If you enjoy building cutting edge platform-ecosystems that gives equal access to financial services to everyone at scale, join us. Software Development Specialties: fintech

Responsibilities

  • ● There are over 5 billion users using basic applications today such email, notes, tasks, calendar and they're not AI-native. Our mission is to build proactive applications for anyone in the world, who are not used to complex prompting. We aim to bring intelligence to conversations, errands, organising and workflows, with minimal to no prompting.
  • ● Our product focuses on achieving high reliability for long-running workflows, persistent context, and real-world task completion. We believe products will greatly reduce hallucinations.
  • ● Our objective is to organise anyone's life, allowing us all to spend time on valuable and meaningful things.
  • ● As an Applied AI Engineer, you will turn model capabilities into real product behavior. You will own problems end-to-end, from shaping model behavior, 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.
  • ● 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 behaviors
  • ● 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
  • ● PyTorch / JAX
  • ● LLMs (OpenAI-style APIs, LLaMA, Qwen, etc.)
  • ● Inference / serving (e.g. vLLM)
  • ● 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)