AI Knowledge Engineer

Kolomolo

22h•Remote

About The Position

At Kolomolo, we are looking for an engineer who lives at the intersection of knowledge graphs, LLMs, and shipping software fast. You will be building systems that map and reason over large codebases, integrating with cloud infrastructure, and turning architectural understanding into working product. This is a hands-on delivery role where you will be using Claude Code as your primary development tool and expected to move at a pace that enables rapid delivery.

Requirements

Deep, practical experience with knowledge graphs: you have built them, not just read about them. You can talk fluently about ontology design, graph traversal strategies, and when a graph model beats a relational or document model
Strong understanding of embeddings - vector spaces, similarity search, chunking strategies, and the trade-offs between embedding-based retrieval and structured graph queries
Solid working knowledge of LLMs: prompt engineering, context window management, tool use, and how to build reliable systems on top of non-deterministic models
Proven ability to ship software quickly
Comfort using AI coding tools (Claude Code specifically) as a daily driver
Architectural-level understanding of Kubernetes, AWS, GCP, and Azure; enough to read a cluster config, understand a VPC layout, parse IAM policies, and know what questions to ask.
Familiarity with IaC tooling: Terraform, Pulumi, or CloudFormation, as structured data sources you can reason over

Nice To Haves

Experience with Neo4j, AuraDB, or similar graph databases in production
Background in static analysis, AST parsing, or code intelligence tooling
Exposure to enterprise software environments with multiple repositories and complex dependency chains
Comfort working asynchronously in a distributed team

Responsibilities

Designing and building knowledge graph pipelines: ingestion, schema design, traversal, and query optimisation (Neo4j / property graphs)
Working with embeddings and LLM APIs to enrich graph-based reasoning
Integrating with real-world infrastructure: pulling context from AWS, GCP, Azure, and Kubernetes clusters, and understanding their architectures well enough to extract meaningful knowledge from them
Working with IaC artifacts (Terraform, Pulumi, CloudFormation) as data sources, parsing, interpreting, and mapping infrastructure-as-code into structured representations
Delivering working software in short cycles using Claude Code as your core development workflow