Software Engineer for Agentic AI (Junior–Mid Level)
Pantas, an AI-native startup in Kuala Lumpur (from a climate/financed-emissions platform), is hiring a junior-to-mid engineer to build agentic AI products like an organizational knowledge base, AI twins, and sustainability agents. Day to day: backend/full-stack work with LLMs, RAG, MCP servers and evals, using Python/Node and AI coding tools, on a hybrid 3-days-in-office schedule.
- Compensation
- Not disclosed
- Location
- Kuala Lumpur, Malaysia
- Work setup
- Hybrid
- Seniority
- Middle
- Employment
- Full time
- Experience
- Not specified
- Category
- Software engineering
- Posted
Skills
Must have
- Build with AI every day (Claude Code, Codex, Cursor or similar) — stated as a hard requirement
- Show something you've built: repo, product, demo, agent or side project people use
- Code in at least one modern backend stack (Python/Django, Node.js or similar) with solid SQL
- Care about quality: clean code, tests, judgment to spot when AI output is wrong
- Comfortable with ambiguity in a fast-moving startup; take feedback and ship again
Nice to have
- Has built something with LLMs: agents, RAG, MCP, evals or prompt orchestration
- Cloud experience (GCP, AWS, Azure) or data/ML infrastructure (Vertex AI, Databricks, SageMaker)
- Strong opinions on context engineering, harness engineering or agentic coding
Full description
Agentic Software Engineer (Junior to Mid Level)
Hybrid: 3 days in the office, 2 days from home
Type: Full-time
We build AI agents that change how work gets done. Come build them with us.
Everyone here builds. Including the CEO. You will never report to someone who only knows how to talk about the work. We judge people by what they ship, not how many years they have on a CV. Bring something you’ve made.
Who we are
Pantas is an AI-native software company in Kuala Lumpur. We started in climate data and became Asia’s first PCAF-accredited financed emissions platform, live with banks and corporates across Southeast Asia. That gives us something most AI startups don’t have: real customers, real data and real problems.
Now we are building the next layer. AI products that do the work for people, not dashboards people have to stare at.
What you’ll build
- Pantas Brain. A company brain inspired by Andrej Karpathy’s LLM Wiki. It turns everything an organisation knows into a living knowledge base that people and agents can use.
- Axsona. AI Twins. Synthetic populations of demographically accurate AI agents that simulate how real audiences react to strategy, messaging and products. Test the message on a simulated population before it hits the real one.
- Agentic sustainability platform. Agents that collect data, flag anomalies and carry a case from one person to the next without anyone chasing emails. We design from the end state and work backwards. We don’t patch yesterday’s workflow.
- We’re done building CRUD. If you want to wire up forms and database tables, this isn’t the job. We build software that reasons over information, uses tools and moves work forward. You’ll build the systems that make it dependable enough for real customers.
Everyone builds. Including the CEO.
No managers who only know how to talk. Our CEO writes code, builds products alongside the team and runs monthly workshops on AI agents, MCP, context engineering, harness engineering and loop engineering. Weekly AI and engineering sharing sessions mean what one person learns, everyone gets. Our engineering leads ship every week.
Everyone is expected to code and build, business development team included. BD colleagues who had never written code have built agents that now do real work.
Your teammates will challenge your thinking, review your work and help you get unstuck. You bring the initiative.
A team that makes you sharper
Our engineering team is 18 strong, with a very high talent density. Three engineers hold a Master’s and one a PhD in Computer Science, from top local and international universities. Expect your peers to raise your game. We’ve built our own proprietary AI algorithms from the ground up. Every month we review the latest research papers and turn what works into how we build.