AI Engineer
About Us
We build AI that helps people work better — not replaces them.
At Maknadata, technology starts with purpose. We take complexity, find its root causes, and turn it into flow through practical, human-centered AI.
AI projects fail when adoption lags behind the tech. We prevent that. You will ensure enterprises don't just deploy AI — they adopt it, trust it, and build business value from it.
As we grow, we're looking for an AI Engineer who wants more than a coordination role — someone who wants to create real impact, learn fast, and help shape how AI is adopted across Indonesia.
What You Will Do
You will bridge technical AI development and business outcomes. Your work sits at the intersection of AI development, business problem-solving, and real adoption outcomes.
- Design, build, and iterate on AI-powered solutions — including context engineering, LLM integration, and AI pipeline development
- Conduct research and experimentation to evaluate AI approaches, tools, and frameworks before committing to implementation
- Translate ambiguous business problems into structured technical hypotheses, then test them
- Monitor and evaluate AI system performance post-deployment, identifying gaps and driving improvements
- Work closely with end users and stakeholders to understand how AI solutions land in practice — not just whether they work technically
- Document experiments, decisions, and learnings in a way the whole team can build on
- Collaborate with cross-functional teams (Product, UX, Engineering) to ensure AI solutions are usable, trusted, and sustainable
- Work with Engineering functions to ensure system reliability, availability and scalability
- Continuously champion high quality software engineering practices - from planning, implementation, test automation, documentation, code reviews, scaling, performance, metrics, logging, and monitoring - essentially the entire software development lifecycle
What We're Looking For
- 0–2 years of software (full-stack) engineering experience — we care more about how you think and learn than how long you've been doing it
- Proficient in any of the major programming or scripting languages (JavaScript, Python)
- Hands-on experience with LLMs, context engineering, or AI/ML frameworks (e.g. vLLM, Langchain, Ollama, OpenAI API, Gemini API)
- Have experience building AI wrapper solutions that integrate with LLM APIs into structured, usable products
- Experience with version control (Git) and working in collaborative codebases
- Demonstrated experience or strong interest in research and development — you're comfortable with uncertainty, iteration, and not having a clear answer upfront
- Strong problem-solving mindset — you define the problem before reaching for a solution
- Collaborative by nature — you work well in cross-functional teams and communicate clearly across technical and non-technical audiences
- Fast learner — you pick up new tools, domains, and contexts quickly and don't need hand-holding to get moving
- Understanding and hands-on experience with inference engineering is a plus
How We Work
We work with the principles of M.A.K.N.A and the 5Cs — not as slogans, but as daily practice.
- Meaningful Impact — every project must solve something real for the client.
- Accountability with Integrity — we own the outcome, not just the task.
- Kolaboratif Growth — we learn fast, mentor each other, and grow together.
- Nurturing Sustainability — we build adoption strategies and processes with long-term value, not quick wins.
- Adaptive Curiosity — we stay curious about what works, challenge norms, and evolve continuously.
- Supported by the 5Cs — Committed, Curious, Collaborative, Credible, and Continuous Improvement — we focus on doing work that matters, bit by bit.
Core Expectations
1. Problem Solver, Not Coder
You approach every challenge by asking what's the right solution before writing a single line of code. You resist the urge to jump to implementation — you understand the problem first, explore the solution space, and build only what's necessary. Code is a tool, not the goal.
2. Ownership and Accountability
You own your work end-to-end — from problem definition to deployment to iteration. When something breaks or underperforms, you investigate and fix it without being asked. You don't hand off and walk away. You stay close to outcomes, not just outputs.
3. Curiosity
You're genuinely interested in how the solutions work, why they fail, and what's possible next. You read, experiment, and ask questions not because it's required, but because you can't help it. You bring ideas into the team that nobody asked for — and that's exactly what we want.
4. Capacity to Learn and Unlearn
You absorb new information fast — new tools, new domains, new feedback. But equally, you're willing to discard what no longer works. You don't cling to past approaches out of habit or ego. When evidence points another way, you adjust.
Why Join Us
- Your work matters. You'll implement AI that real users rely on, not POCs that never launch. You'll see adoption succeed or struggle, learn why, and get better.
- Fast learning curve. You'll see end-to-end AI delivery — from discovery to production to adoption. You'll understand the full picture, not just one piece.
- Ownership. In a small, growing team, your contribution directly shapes whether projects succeed or fail. You won't hand off work; you'll see it through.
- Room to experiment. We value initiative, curiosity, and thoughtful problem-solving. You'll propose solutions, test ideas, and refine your approach.
- Mission-driven culture. We build with intention and clarity. Adoption isn't an afterthought; it's the whole point.
- Hybrid/flexible work. Remote by default, with Tuesday office days at GoWork and quarterly all-hands. Flexibility when you need it, collaboration when it matters.
- Learning from real projects. Every client project becomes a lesson on what builds trust, what drives adoption, and what sustainable change looks like.
What Success Looks Like
Month 1: Understand the Foundation
You've onboarded into the team's tools, codebase, and ways of working. You understand how Maknadata approaches AI development — from problem framing to solution design to deployment. You've explored at least one existing AI solution end-to-end, can articulate how it works, where it breaks, and why certain decisions were made. You ask good questions and document what you learn.
Month 2: Contribute to a Real Problem
You're no longer just observing — you're building. You've taken ownership of a defined scope within an active project, whether that's a prompt pipeline, an LLM integration, or an experimentation setup. You've run at least one structured experiment, documented your hypothesis, findings, and next steps. Your teammates can follow your thinking without needing to ask you to explain it.
Month 3: Deliver and Iterate
You've shipped something that real users interact with. You're monitoring its performance, identifying gaps, and proposing improvements based on evidence — not assumptions. You understand the full AI development workflow: from business problems to solution design, experimentation, deployment, and adoption. Your peers trust your judgment. Your stakeholders see you as someone who owns outcomes, not just tasks.
Interested
Tell us:
What draws you to this role (Why adoption, why AI, why Makna)
A time you bridged technical and business teams — what was the tension, and how did you navigate it
We move fast. If you're ready to build AI that actually gets used, let's talk.
Help us build AI in bits now, scale adoption tomorrow.
