AI Software Engineer

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About the role

About ExTrac ExTrac is a decision intelligence company used by governments, defence organisations, financial institutions, and corporates operating in complex, fast-moving environments. Our capabilities fuse curated data sources, domain-specific AI, and deep human expertise to transform information overload into clear, actionable foresight. Our ambition is to become the analytical backbone that organisations rely on when geopolitical uncertainty becomes an opportunity or a strategic risk. More at extrac.ai . The Role We are looking for a Software Engineer to join ExTrac's AI team, building Co-Analyst and the analytical AI features around it. Co-Analyst is a user-facing multi-agent system that works alongside intelligence analysts to research and write reports. A planning loop decomposes an analyst's question, fans work out to sub-agents, and assembles the results into a report where every claim traces back to the chunk of source it came from. Underneath sits hybrid retrieval over a large unstructured corpus: analyst intent translated into structured filters, combined with keyword and dense vector search, across multiple languages and media types. The agent work is the centrepiece but not the whole job. In a single quarter the work spans agent orchestration, retrieval, graph analytics, and long-running streaming pipelines, alongside the services and databases underneath them. You will own well-defined features and components end to end across a Python and Go codebase, working alongside senior engineers who set technical direction, the data team who own the ingestion pipelines, and a research-focused ML team who train and evaluate the models we integrate and serve. The loop is short: product brings an idea, often recent and unproven, and our job is to spike an implementation and take it to a production feature. New features land close to weekly. This hire exists to add capacity on the AI team's hard problems: an engineer who can independently deliver well-scoped features and components, and who is building towards owning more of the system end to end. What the job involves Agentic and analytical AI features • Build and improve components of the agent loop itself: context assembly, tool selection, and sub-agent orchestration, with guidance from senior engineers on the team. • Build the analytical AI features that sit alongside it, from network construction through to the summaries analysts read. • Help prove that changes are improvements, running experiments against live analyst traffic behind feature flags. • Contribute to agreeing what "better" means for a capability, and flag honestly when the evidence says a promising approach is not working. • Work with embeddings as more than a retrieval concern. The same vectors drive network construction and community detection. • Work within a model-agnostic design, swapping models and embeddings on the back of the ML team's evaluations rather than being locked to one. Service design and delivery • Take a well-defined problem, clarify requirements with your lead or a senior engineer, and ship it to production with regular check-ins rather than close oversight. • Own features and components end to end within a single system, contributing to system design and architecture discussions and taking on more of the design work as you build context. • Build and maintain APIs used by internal teams and customers, following established contracts and versioning conventions. • Work with the storage layer as a design concern rather than an implementation detail: schema, indexing strategy, and access patterns, across relational, document, and vector stores. Production engineering • Build and operate supporting services across Python and Go, with growing ownership of design, deployment, and o

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