Founding Engineer, Head of Applied AI
About the role
EquiEqui is an investment management firm building institutional-quality alternative investment solutions. We operate a unique business model: in addition to offering Equi-branded funds, we build white-label fund of funds products for some of the top independent multi-family offices and financial advisory firms in the country. We combine deep manager selection expertise with sophisticated portfolio construction to deliver differentiated outcomes for our clients and partners.
The firm is at an inflection point, our AUM is scaling rapidly, we've signed an LOI to launch an additional fund of funds product, and we expect to sign more LOIs in the coming months. We're assembling a small, high-caliber team that can grow with the platform and take on increasing responsibility as we scale.
About Equi
Equi is a team-first environment where transparency and healthy debate are integral to our success. We operate with a flat structure, ideas win on merit, not seniority, and every team member is expected to contribute to the conversation. We value intellectual honesty, rigorous thinking, and the humility to change your mind when the evidence warrants it. Our vision for engineering is to keep the team small and flat, where everyone remains involved in building rather than adding layers of management.
What You'll Do
Lead Builder Across the Company
Serve as the primary engineer and technical leader, building software solutions that accelerate growth across all teams
Partner directly with the Investment Team, Operations, and GTM to identify high-impact opportunities for technology and automation
Exercise significant autonomy, taking ownership of complex projects from conception through post-launch iteration
Make critical technical decisions and architect scalable solutions for a growing platform
Customer-Facing Applications
Translate complex user needs and business requirements into well-architected, scalable, maintainable, and user-friendly full-stack applications (Typescript, Next.js, Python, Postgres on GCP)
Design, implement, and maintain performant and reliable APIs and backend services
Develop exceptional user experiences for clients and partners
AI Engineering
Ingest multi-format investment data and parse it into both human- and computer-digestible formats
Use AI to perform investment analysis and provide recommendations to the human investment team
Build workflows to automate human actions across the investment process
Develop RAG-based intelligence systems to power internal and external applications
Team Leadership & Growth
Act as the backbone of the engineering function, establishing technical direction and best practices
Lead technical decision-making and architectural design discussions
Proactively identify and advocate for process improvements and technology adoption
Eventually grow and lead the engineering team while maintaining hands-on building responsibilities
Who You Are
Required Experience:
5+ years of professional experience in software development, demonstrating progressive responsibility and impact
Passion for investing: Experience can’t substitute for an innate curiosity and passion for the world of investing. We operate in the depths of it, so you better love it!
Full-Stack Proficiency: Proven track record of designing, building, deploying, operating, and maintaining complex full-stack web applications in production environments. Strong proficiency in Typescript, Next.js, and Python
Data & Cloud Foundation: Solid understanding of data pipeline concepts (ETL/ELT) and data warehousing principles
AI-Driven Engineering: Competent in AI-driven engineering workflows and can use them to deliver effectively; experience building AI-powered applications
Product Acumen: Demonstrated ability to think critically about product strategy, understand user needs and pain points, and effectively translate them into technical solutions
Autonomy & Action Orientation: Proven ability to work independently with minimal supervision, navigate ambiguity effectively, and drive projects to completion with a strong sense of ownership and a bias for action
Collaboration & Communication: Excellent verbal and written communication skills, with the ability to articulate complex technical ideas clearly to both technical and non-technical audiences
Leadership Potential: Desire and ability to eventually grow and lead a small, high-performing engineering team
Preferred
Prior experience working in fintech, investment technology, or financial services
Specific experience designing and scaling large-volume data pipelines or complex distributed systems on GCP
Experience using containerization technologies like Docker and API middleware like GraphQL
Familiarity with advanced data modeling techniques
Experience with RAG systems, LLM integration, or AI agent development
Defining Our Ideal Candidate
Key Traits in Action
Product-Minded: Deeply curious about the 'why', why are we building this feature? Who is it for? What problem does it solve? Engage with stakeholders across the company, offer thoughtful suggestions, challenge assumptions constructively, and propose alternative approaches based on technical insight and understanding of user needs. Evaluate tradeoffs between engineering effort and product impact, leverage data to support perspectives, ensuring we build truly valuable solutions.
High Autonomy: Freedom to determine the best way to tackle complex problems and execute tasks within agreed-upon goals. Self-starters, comfortable navigating ambiguity, proactive in seeking information, owning work from end-to-end. Freedom paired with accountability; engineers own decisions and outcomes, learning and adapting from successes and setbacks in a psychologically safe environment.
Bias Toward Action: Valuing progress and learning through doing, rather than over-analysis or pursuit of unattainable perfection. Adept at making calculated decisions with incomplete information, understanding reversibility of choices, iterating for better results. Identify and remove roadblocks, break down large problems into manageable steps, readily experiment, use feedback to adjust course quickly, ensuring continuous delivery of value to customers.