Software Engineer - AI Research

Design and deliver the intelligence layer that turns enterprise process knowledge, documents, interviews, system logs, into a governed, provenance-carrying process ontology, and the AI systems that extract, consolidate, ground, and continuously learn from it. Ensure every output is evidence-backed; hallucination is a product failure, not a metric.

Build end-to-end pipelines for ingesting complex documents, handling layouts, tables, and scanned diagrams, and generate robust embeddings for long-form content

Design schema-constrained LLM extraction frameworks using typed schemas, validation loops, and automated repair/re-prompt cycles

Own retrieval engineering solutions, integrating hybrid search, reranking, and query expansion to surface relevant context efficiently

Deliver knowledge-graph construction and entity resolution pipelines, modeling property-graphs in Neo4j or equivalent stores and enabling GraphRAG retrieval

Improve grounding mechanics by integrating terminology stores and retrieval-in-the-loop generation to ensure evidence-backed outputs

Collaborate with cross-functional product and research partners to define evaluation datasets, grading rubrics, and regression gates, feeding field insights back into the roadmap

Build multi-agent orchestration using planner/worker patterns and tool routing on native SDKs, ensuring seamless state sharing and coordination

Own MCP and agent-to-agent integration, implementing secure contracts, authentication boundaries, and context passing between services

Proven expertise delivering production-grade AI solutions in Python, with strong typing, test coverage, and async pipeline design

AI-native velocity as a default mode of working

Deep knowledge of large-language-model prompting, schema-driven extraction, and automated repair loops

Mastery of retrieval techniques, including dense embeddings, hybrid search, reranking, and query expansion

Experience building and operating property-graph databases such as Neo4j, and implementing entity resolution and GraphRAG pipelines

Hands-on competence with grounding architectures, terminology stores, and retrieval-in-the-loop generation

Proficiency in multi-agent orchestration frameworks (e.g., LangGraph, OpenAI Agents SDK) and secure MCP or A2A integration patterns

Track record of defining evaluation datasets, constructing graders, and establishing regression testing with tracing tools like Langfuse

Strong judgment to balance research ambition with product constraints, and clear communication with engineers, product managers, and senior stakeholders

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.