AI Research – kAIgentic

Staff Engineer, AI Research

About kAIgentic

kAIgentic is building the intelligence layer for the world’s most ambitious enterprises. Headquartered in Singapore with teams in India and Japan, we help 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 a Staff Engineer on the AI Research team, you will be the technical authority for AI systems across kAIgentic’s enterprise platform. You’ll define how we build AI that enterprises can trust—accurate, explainable, auditable, and reliable in high-stakes financial workflows.

What You’ll Do

Define the long-term vision for kAIgentic’s intelligence layer architecture; architect the enterprise knowledge platform that ingests, understands, and operationalizes organizational knowledge at scale
Design the RAG architecture supporting complex retrieval patterns across heterogeneous enterprise data sources; own the AI accuracy strategy establishing the frameworks, metrics, and systems that ensure enterprise grade reliability
Define the hallucination elimination approach combining retrieval grounding, output validation, selfcorrection, and human oversight; architect the knowledge extraction pipeline transforming unstructured enterprise processes into structured, executable AI workflows
Design the evaluation platform enabling systematic, continuous measurement of AI quality across all production systems; define the AI observability architecture providing deep visibility into model behavior, retrieval quality, and system performance
Establish prompt engineering standards and patterns adopted across all AI features; drive the AI governance framework ensuring auditability and compliance for regulated deployments
Influence AI direction across all product teams; evaluate emerging models, techniques, and research for enterprise applicability; mentor leads and senior engineers on AI system architecture; represent kAIgentic’s AI capabilities to enterprise customers and partners

What You’ll Bring

12+ years of experience in software engineering with deep expertise in AI/ML systems; AI-native velocity as a default mode of working (mandatory); expert-level proficiency in Python; strong knowledge of Go a plus
Deep expertise in 4+ of the following:; RAG architecture and retrieval systems at enterprise scale; document AI, NLP, and knowledge extraction; LLM application architecture and production deployment
AI evaluation, quality assurance, and testing at scale; hallucination detection and mitigation strategies; AI observability platforms and debugging
Guardrails, output validation, and compliance systems; knowledge graphs and semantic systems; AI for regulated industries (finance, healthcare, legal); proven track record of building AI systems trusted in high-stakes environments
Experience translating research advances into production capabilities; strong publication record or equivalent demonstrated thought leadership (preferred)
Exceptional architectural judgment and communication skills; ability to explain AI capabilities and limitations to business stakeholders

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 building the substrate that turns institutional knowledge into governed, production-grade operations. This is not a wrapper on a model. It 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: an intelligence layer that helps enterprises continuously improve how they run.

Software Engineer-III, AI Research

About kAIgentic

The Role

As a Lead Engineer on the AI Research team, you will drive the technical direction of kAIgentic’s intelligence layer. You’ll architect the systems that transform enterprise knowledge into AI-powered workflows—ensuring accuracy, auditability, and reliability in environments where AI mistakes have real consequence

What You’ll Do

Architect the RAG platform supporting diverse enterprise document types and retrieval patterns; design the knowledge extraction pipeline that converts SOPs, policies, and procedures into structured,executable representations
Define the evaluation strategy ensuring AI outputs meet enterprise accuracy standards with systematic measurement of grounding and hallucination; own the self-correction architecture implementing validation loops that catch and fix LLM errors before they reach users
Design the prompt engineering framework establishing patterns, templates, and versioning for consistent AI behavior; architect AI observability using Langfuse and Arize Phoenix for deep tracing, evaluation, and debugging
Define retrieval strategies balancing accuracy, latency, and cost for production workloads; build guardrail systems ensuring AI outputs comply with enterprise and regulatory requirements
Lead technical execution across the AI Research team; make build-vs-buy decisions for AI components and evaluate emerging models and techniques
Mentor senior engineers on AI system design; drive AI-native development practices to accelerate research-to-production cycles

What You’ll Bring

8+ years of experience in software engineering with significant focus on AI/ML; expert AI-native development capability (mandatory)
Strong expertise in Python; working knowledge of Go a plus; deep expertise in 3+ of the following:
RAG systems at scale (retrieval optimization, hybrid search, reranking); document AI and enterprise NLP; LLM application architecture and prompt engineering
AI evaluation and quality assurance frameworks; knowledge extraction and structured output generation
Production ML systems and MLOps; AI observability and debugging; guardrails and output validation systems; experience building AI for regulated industries (finance, healthcare, legal)
Track record of taking AI systems from research to production; strong architectural thinking and communication skills

Why join kAIgentic?

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 building the substrate that turns institutional knowledge into governed, production-grade operations. This is not a wrapper on a model. It 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: an intelligence layer that helps enterprises continuously improve how they run.

Software Engineer-II, AI Research

About kAIgentic

The Role

As a Senior Software Engineer on the AI Research team, you will own critical components of kAIgentic’s intelligence layer. You’ll design RAG architectures, build knowledge extraction systems, and ensure our AI delivers accurate, grounded outputs in regulated enterprise environments where hallucinations are not acceptable.

What You’ll Do

Design and implement production RAG pipelines with advanced retrieval strategies (hybrid search, reranking, query expansion)
Build document understanding systems that extract structure and meaning from complex enterprise documents; develop knowledge extraction pipelines that transform SOPs and procedures into executable workflow representations
Implement self-correction loops that validate LLM outputs against schemas and re-prompt on failures; design evaluation frameworks measuring grounding, relevance, accuracy, and hallucination rates
Build observability for AI pipelines using Langfuse or Arize Phoenix; optimize retrieval and generation for latency and cost in production
Implement guardrails and output validation for enterprise compliance; develop prompt libraries and engineering patterns for consistent quality
Own AI features end-to-end from research prototype to production deployment; drive AI-native development practices across the team

What You’ll Bring

4+ years of experience building applied AI/ML systems; expert AI-native development capability (mandatory)
Strong proficiency in Python; deep expertise in 2-3 of the following:; RAG architecture and retrieval optimization
Document intelligence and NLP (layout understanding, entity extraction, summarization); LLM application development and prompt engineering
Embedding models and vector search optimization; AI evaluation frameworks and systematic testing
Production ML systems and observability; knowledge graphs and structured extraction
Experience deploying AI systems in production; strong engineering fundamentals and code quality standards

Why join kAIgentic?

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 building the substrate that turns institutional knowledge into governed, production-grade operations. This is not a wrapper on a model. It 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: an intelligence layer that helps enterprises continuously improve how they run.