kAIgentic is building the intelligence layer for the world's most ambitious enterprises. Headquartered in Singapore with teams in India and Japan, our software platform helps large organizations evolve as fast as technology itself by turning the tacit know-how locked inside their people into safe, governed, AI-powered operations.
The hardest part of enterprise transformation is not strategy. It is execution. Institutional knowledge lives in people's heads, systems are fragmented, and risk tolerance is low. kAIgentic captures how work actually happens, designs better workflows, and runs them inside an intelligence layer that is observable, auditable, and engineered for the most regulated environments on earth. The outcome is an enterprise that continuously improves.
We are backed by SMBC Group as our founding partner and customer zero, and our platform is already being proven inside one of the most complex, regulated operating environments in the world. That means real problems, real data, and real production impact from Day 1.
The Role
As our first Product Technical Support Engineer, you're the technical voice for customers who are putting agentic AI into production. You'll spend your time diagnosing why integrations break, why agents behave unexpectedly, or why a workflow isn't scaling the way they planned. You'll read logs, trace distributed systems, and explain what's actually happening. Sometimes that's a product limitation we need to fix. Sometimes it's a configuration problem or a use case that needs to be structured differently. Your job is to figure out which one it is, help the customer move forward, and make sure engineering knows about the patterns you're seeing. This is early enough that you'll help define what technical support looks like at an agentic AI company. There's no playbook. That's the point.
What You'll Do
Investigate customer issues that involve code, APIs, logs, and deployed systems. You'll read API traces, follow execution paths through workflows, and figure out where the breakdown happened.
Work with customers to understand what they're trying to build with our agents. Sometimes the
problem isn't broken product, it's that they're using it for something it's not yet designed for.
Escalate to engineering with clear reproduction steps, data, and context. You're the filter between raw customer reports and engineering's time, so this needs to be tight and precise.
Build tooling and documentation that lets the next customer with the same problem find the answer themselves. This starts lightweight and becomes a knowledge base as patterns emerge.
Help customers actually get value from the product, not just unblock them. This might mean sitting in technical deep dives, writing implementation guides, or explaining how to restructure their prompts for better results.
Feed product signals back to the team. You'll see patterns in what customers struggle with first, and you'll have opinions about what gaps in the product matter most.
What You'll Bring
You have experience doing technical work that spans systems thinking, customer communication, and operational judgment. Here's what we're looking for: You can read code.
You don't need to write production code every day, but you need to understand it, trace through it, and explain what it's doing to someone else. Comfortable with APIs, logs, distributed systems, and debugging when things are unclear. You ask good questions instead of assuming.
You have a working model of how LLMs work, what they're good at, and why they fail in predictable ways. You don't need to be a machine learning researcher. You need to know the difference between a hallucination, a prompt issue, a temperature problem, and a knowledge cutoff problem. You can talk to customers without talking down to them or over their heads.
You adapt whether you're speaking to technical founders or business operations people. You explain what's happening without either oversimplifying or burying them in jargon. You're okay with ambiguity. We don't have all the answers about what this product should be.
You help shape that by noticing problems, proposing solutions, and pushing back when something doesn't make sense.
You care about customers actually succeeding, not just closing tickets. You'll spend time on the harder problem if it's the right one to solve.
You'll suggest a different approach if that's better for them. You can work asynchronously and independently. A lot of customer debugging happens on their schedule, in their systems, across time zones. You're comfortable with that.
Strong plus if you have:
Prior experience in a customer-facing technical role at an early-stage company. You know the balance between moving fast and not leaving customers broken.
Python or a similar language so you can write small debugging scripts or automation without waiting for engineering.
Experience with observability tooling, APMs, or infrastructure debugging.
You've built or shipped something with an LLM. Understanding the actual pain points of working with LLMs on real data and real customers matters.
Familiarity with financial services or enterprise operations. SMBC is our first customer, and banking comes with operational rigor that's different from consumer products.
Why join kAIgentic?
We are a global team of builders who thrive in ambiguity, care deeply about the customers we serve, and believe the intelligence layer is how enterprise work will be reshaped over the next decade. We are building the connective tissue that lets large companies operate with the speed of a startup and the trust of an institution.
We look for people who:
Combine technical excellence with genuine customer empathy.
Are entrepreneurial and energized by zero-to-one problems with no playbook.
Lead with ownership, integrity, and collaboration, not titles.
Want to help define a new category of enterprise AI, not just ship inside an existing one.
Working here means being surrounded by peers who challenge assumptions, celebrate progress, and build with both courage and care.
Life at kAIgentic
Intelligence layer at the core. You will be shaping the substrate that turns institutional knowledge into governed, production-grade operations. This is enterprise infrastructure with real consequences.
Innovation at enterprise scale. Startup velocity meets the depth, scale, and stakes of mission-critical, regulated environments. Both are non-negotiable.
Ownership from Day One. Your work directly shapes the product, the culture, and the outcomes our customers see.
Learning and growth. You will work alongside seasoned leaders from leading enterprises who have built and scaled global businesses.
A culture of trust. Psychological safety, transparent disagreement, and disciplined experimentation are how we operate, not slogans on a wall.
Global collaboration. Teams across Singapore, India, Japan, Europe, and the US, working as one.
A mission worth the effort. Building something the world has not seen before: anintelligence layer that helps enterprises continuously improve how they run.
