About The Position

Solovis is a leading portfolio management and analytics platform helping institutional investors navigate today's complex global markets with clarity and confidence. Backed by Insight Partners, we are building the next chapter of growth by investing in people and product to raise the bar on quality and client outcomes. Our team is driven by a culture of disciplined execution, humility, and curiosity where AI is at the core of how we operate, innovate, and serve clients. At Solovis, you will join a tech-forward, growth-minded team that believes in learning fast, thinking big, and delivering meaningful impact for asset owners worldwide. Our companies are not the largest or flashiest, but they are among the best-run software businesses, creating value for customers and shareholders at an accelerated pace.

Requirements

  • 5 to 9 years of professional software engineering experience
  • Strong fluency in Java or Python on AWS, or C# on Azure
  • Production ownership history: you have shipped non-trivial systems and been accountable when things went wrong
  • At least one significant out-of-depth experience, whether a stack switch, a domain change, or a greenfield build in unfamiliar territory
  • 6 or more 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 production features and modernizations across our stack using agentic development as your primary workflow
  • Work in brownfield environments, adding new functionality to existing code with the same AI-native discipline as greenfield work
  • Build out and operate multi-agent workflows where distinct roles (requirements, architecture, testing, development) operate in parallel and in coordination
  • Pair with existing engineers on real problems to transfer agentic working habits in practice
  • Validate agent output continuously, knowing when to intervene and when to let work run based on how well requirements were established upfront
  • Contribute to timely sprint delivery and consistently meet or exceed release schedule targets
  • Maintain software quality by managing escaped defects and contributing to backlog reduction over time
  • Produce scalable, efficient, and well-documented code
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