Principal Engineer, AI Research

As Principal Engineer, AI Research, you will be a senior technical owner for how kAIgentic builds reliable, auditable, production-grade AI systems for enterprise environments.

Working closely with the CTO, leadership, and engineering teams, you will translate company-level AI strategy into research-backed systems that can operate inside regulated industries. You will make high-judgment technical decisions across retrieval, knowledge extraction, ontology design, evaluation, grounding, hallucination mitigation, and model behavior in production.

This role is for someone who can read the AI frontier with depth, separate durable technical signal from market noise, and convert the right ideas into reliable, production-grade systems that can operate in high-trust enterprise environments.

You will shape how teams evaluate AI quality, measure reliability, reduce hallucination risk, and build intelligence-layer capabilities that are observable, explainable, and production-ready. You may also support high-trust technical conversations with banking partners, enterprise customers, and regulators where deep AI systems expertise is required.

The work sits at the intersection of applied AI research, enterprise AI infrastructure, and regulated production environments. You may lead a lean, embedded research engineering team on the highest-priority AI platform programs, while partnering closely with the CTO on broader AI direction.

Responsible for the AI research behind kAIgentic’s intelligence layer, translating company-level AI strategy into focused research directions, rigorous experiments, evaluation frameworks, and insights that strengthen enterprise AI capabilities.

Make high-judgment technical decisions across retrieval architecture, knowledge extraction, grounding, hallucination mitigation, model evaluation, output validation, and AI system reliability.

Design and evolve the domain ontology behind kAIgentic's intelligence layer, including entity resolution across heterogeneous enterprise documents, schema extraction from regulated sources, and ontology evolution as the enterprise itself changes.

Design and build research-to-production pipelines that help kAIgentic evaluate emerging AI techniques, de-risk adoption, and ship them safely into regulated enterprise environments.

Solve hard AI reliability problems across multi-hop reasoning, cross-document consistency, retrieval quality, output determinism under model non-determinism, self-correction, and production latency constraints.

Raise the technical bar through research and architecture reviews, mentor across the org, partner deeply with AI Infrastructure, Product, Engineering, Security, and customer-facing teams, and lead a lean embedded team on the highest-priority AI platform programs.

Deep applied AI, ML, or AI systems experience, with a proven track record of turning research into production systems that deliver reliable outcomes in high-stakes enterprise environments.

Expert-level Python, with strong ability to work across modern AI engineering stacks. Working knowledge of Go is a plus.

Deep expertise across several of these areas: RAG architecture, retrieval systems, reranking, document AI, enterprise NLP, hallucination detection and mitigation, LLM evaluation, grounding measurement, adversarial testing, knowledge extraction, ontology design and management, AI observability, guardrails, output validation, fine-tuning, and domain-specific model adaptation.

Experience building systems that require auditability, reliability, explainability, recoverability, operational trust, and enterprise-grade governance.

AI-native velocity as a default mode of working, with the ability to communicate complex AI system decisions clearly across engineering, product, executive, customer, and compliance audiences.

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.

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.