Senior AI Software Engineer

ExTrac is a decision intelligence company that combines curated data, domain-specific AI, and human expertise to provide actionable foresight for organizations operating in complex environments. The Senior AI Software Engineer will build and operate agentic and analytical AI features for Co-Analyst, owning services, APIs, databases, streaming pipelines, and production infrastructure across Python and Go. The role also involves collaborating with analysts, product, and the ML team to evaluate, integrate, and deliver reliable AI capabilities.

Responsibilities:

• 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
• Prove that changes are improvements, running experiments against live analyst traffic behind feature flags
• 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
• Find workable approaches where no established pattern fits, on a dataset that rarely arrives clean
• 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
• Take an ambiguous problem, gather requirements, write a technical design, and ship to production with minimal oversight
• Own services end to end, including consolidating or decommissioning what they replace
• Design APIs used by both internal teams and customers, and hold them to clear contracts and sensible versioning as the number of consumers grows
• 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
• Contribute to and lead system design and architecture decisions
• Own supporting services end to end across Python and Go: design, build, deploy, operate
• Hold agent workflows to production standards for latency, cost, and reliability, in a system where non-determinism is a given
• Build and operate long-running streaming pipelines, including the caching and recovery behaviour that makes them survivable
• Work directly with the analysts who use our products, turning what they hit in practice into changes in the system
• Iterate quickly against a live stream of product requests, flagging where they collide with longer-horizon capability work
• 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
• Integrate and serve the models they train, and build the production infrastructure their evaluation frameworks run on