Senior Software Engineer (Front End) - Hybrid in London

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.

We are looking for a Senior 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 vector search over a large unstructured corpus, across multiple languages and media types.

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 services end to end across a Python and Go codebase, working alongside 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. This hire exists to raise the AI team's throughput on hard problems, with an engineer who brings the systems depth to take an AI capability from something that works to something analysts can rely on.

• Agentic and analytical AI features
• Build and improve the agent loop itself: context assembly, tool selection, and sub-agent orchestration.
• Build the analytical AI features that sit alongside it, from network construction through to the summaries analysts read.
• Agree what 'better' means for a capability before shipping it, and make the call honestly when the evidence says a promising approach is not working.
• 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.
• Take an ambiguous problem, gather requirements, write a technical design, and ship to production with minimal oversight.
• Treat the storage layer as a design concern rather than an implementation detail: schema, indexing strategy, and access patterns, across relational, document, and vector stores.
• Own supporting services end to end across Python and Go: design, build, deploy, operate.

Working with analysts, product, and the ML team

• Partner with the ML team on agentic approaches, taking proven concepts to production and solving the engineering, performance, and reliability problems a research implementation does not have to.
• You will spend more of your time in Python and Go services, databases, and APIs than in a prompt file.
• You will be trusted to lead projects, make decisions, and drive outcomes.
• Experience with them is useful, but we build most of our own orchestration, because off-the-shelf abstractions have not survived our requirements around evaluation, control, and production performance.
• You can take a recent technique or paper, spike an implementation, and give a clear recommendation on whether it is worth taking further.
• You are as comfortable arguing to kill something as to ship it.
• You treat quality, security, and observability as engineering fundamentals rather than optional extras, and you make the case for addressing technical debt rather than living with it.
• You raise the bar for the engineers around you in ways they would recognise: pairing on problems outside your own project, leaving reviews people learn from, and writing things down for the next person.

Our software sits underneath decisions taken by governments, defence organisations, and institutions operating where being wrong or late carries real consequences. Strong performance means larger sections of Co-Analyst, more architectural say over how the agent system evolves, and more influence over which ideas we pursue rather than only how we build them. There are established patterns for running web services and for training models. Making our systems work inside FedRAMP environments is a major upcoming project, and engineering under that kind of constraint is a skill set that stays with you.

Due to the nature of our work and the clients we support, applicants must be eligible to obtain UK security clearance.

Qualifications

• 4+ years of professional software engineering experience, with demonstrated ability to stand up production-grade services with comprehensive test coverage, and experience owning features end to end.
• Proficiency in building services in Python, with working knowledge of Go or the ability to pick it up quickly.
• Experience building and operating agentic systems, LLM applications, or production retrieval that real users depend on.
• Solid understanding of distributed systems, databases, and software engineering patterns.
• Experience writing performant asynchronous code that scales under real workloads.
• Experience with feature flags and trunk-based deployment.
• Able to take a PRD and scope a technical design from it, pushing back where the proposed approach does not hold up. Comfortable writing clear technical documentation and discussing requirements with colleagues from engineering and the wider team.
• Breadth across the stack: improving deployment pipelines, understanding database behaviour, and a genuine interest in security and AI guardrails.
• Experience with retrieval systems and large-scale vector database performance (Elastic).
• Experience with graph or network analysis at scale.
• Experience building retrieval or analysis that works across multiple languages.
• Experience operating multi-tenant systems where data isolation is a hard requirement.

A significant upcoming project is making our systems work within FedRAMP environments.

Benefits

• Competitive salary based on skills and experience.
• A generous benefits package, including Private Medical Health Insurance and enhanced pension contributions.
• Enhanced parental leave and a workplace nursery scheme.
• 33 days of leave across the year inclusive of bank holidays.
• Flexible working. The team is typically in our central London office two days a week, and you are welcome to come in up to five.

ExTrac AI provides equal employment opportunities (EEO) to all employees and applicants for employment without regard to race, colour, religion, sex, national origin, age, disability, genetic information, sexual orientation, gender identity, or gender expression. ExTrac AI is committed to a fair and transparent hiring process. Please be advised that we may use artificial intelligence-driven tools to assist our recruitment team in screening, assessing, and selecting candidates for this position but all hiring decisions will be made by a member of our team.