About The Position

Solovis builds portfolio management and analytics software for institutional investors. We are backed by Insight Partners and growing fast, investing in product, people, and the technology that drives client outcomes. We've made a company-wide commitment to be AI-native by end of 2025. Engineering is at the center of this transformation. We're building out our agentic development team and hiring engineers who are already working this way in production. This is a hands-on engineering role where you own features end-to-end, ship production systems using agentic workflows as your default mode of work, and take genuine accountability for what you build. You create your own momentum through strong requirements discipline, continuous validation of agent output, and a concrete understanding of when to push forward and when to stop and reset. Your stakeholders are sophisticated and expect quality, reliability, and engineers who think beyond the ticket.

Requirements

  • 5–9 years of professional software engineering experience
  • Strong fluency in Java or Python on AWS, or C# on Azure
  • Production ownership history: you've shipped non-trivial systems and been accountable when things went wrong
  • At least one significant out-of-depth experience — a stack switch, a domain change, or a greenfield build in unfamiliar territory
  • 6+ months of serious, production-grade agentic tool use (Claude Code, Cursor, or equivalent) — production work only, not pilots or experiments
  • Demonstrated ability to build product with AI, not just use AI within existing workflows
  • Concrete understanding of when to let an agent run versus when to intervene, grounded in how well requirements were established before the work started
  • Experience reviewing and validating work you did not write, including agent-generated output
  • Familiarity with building multi-agent frameworks where distinct roles operate concurrently

Responsibilities

  • Deliver features and modernizations across our stack using agentic development end-to-end
  • Build and operate multi-agent workflows where distinct roles run in parallel and in coordination
  • Work in brownfield environments with the same discipline as greenfield builds
  • Validate agent output and know when to intervene — grounded in how well requirements were set upfront
  • Pair with existing engineers to transfer agentic working habits through real production work
  • Contribute consistently to sprint delivery and take ownership of quality over time
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