DealMaker is a fast-growing fintech company revolutionizing the capital markets ecosystem with a mission to make online capital raising mainstream. We empower founders, CEOs, and operators to raise capital digitally, both from their own communities and through strategically marketed campaigns. No other platform provides an end-to-end solution like ours—and our track record speaks for itself, with over $2B raised across 1,000+ campaigns. We power the largest online capital raises for customers like the Green Bay Packers ($65M), Miso Robotics ($72M+), Monogram Orthopaedics (Nasdaq:MGRM) and many others, with 3 IPOs in the past year alone. We are quickly expanding our horizons and are seeking talented team members to join us on our journey to transform the global capital markets.
Who you are
You're a builder who thrives on turning AI possibilities into customer value. You’re bold and direct in your technical decisions, and you find a way to make AI work in production even when the path isn't clear. You ship functional AI features quickly, automate complex workflows, and solve hard problems by choosing pragmatic solutions over over-engineered ones, like opting for prompt engineering over full model fine-tuning when speed and cost matter most. You also embody our core values. You prioritize speed over perfection and iterate based on real user feedback. You obsess over customers every AI feature you build is driven by their needs and makes their experience meaningfully better. You focus on measurable outcomes over technical elegance, push your limits, and continuously learn to stay ahead in a fast-moving field.
What you will do
The most critical part of this role is to deliver AI-based features into our customer-facing product . You'll architect and implement AI capabilities that directly enhance how our customers raise capital and manage investor relations. This includes both building net-new LLM-powered features and improving existing ones, such as our customer-service chatbot, to automate complex workflows, guide users through fundraising processes, and extract insights from financial documents.
You'll collaborate closely with the Data & AI team in the creation and usage of MLOps systems, contributing to the design and integration of tools for model deployment, observability, monitoring, retraining, and continuous performance improvement. Your focus will be on ensuring that AI features operate reliably at scale and can evolve quickly based on user needs.
In addition to working with Product and Design, you’ll also partner with data teams across a variety of scenarios to translate business needs into technical AI solutions. You’ll mentor other engineers on AI best practices and drive key technical decisions that shape our broader AI strategy. Your work will directly impact thousands of users who rely on our platform to navigate complex financial processes.
What skills you need
- Advanced Python - Expert-level proficiency, including experience with AI/ML libraries such as LangChain, Hugging Face Transformers, or similar frameworks commonly used in production-grade AI systems.
- Architectural Design & System Ownership - Experience designing and owning end-to-end AI systems in production
- LLM Ops - Hands-on experience with model deployment, monitoring, fine-tuning, and optimization
- Agent Design Patterns - Proficiency with ReAct, tool-use, and planning agents for complex workflows
- 2+ years shipping AI features in customer-facing products
- Experience with cloud platforms (AWS, GCP, Azure) and containerization
- Strong collaboration skills for working with product, design, and engineering teams
- Startup mindset with bias toward action and customer impact
Bonus Points
- ADK (Agent Development Kit) experience
- Multi-Modal Agents: Experience exposing AI agent capabilities through different mediums such as web-based chat, Slack, email, etc.
- Experience in fintech or compliance-bound domains
- Knowledge of vector databases and semantic search
- Familiarity with AI safety and responsible AI practices
- ADK (Agent Development Kit) experience
- MCP server and client creation, A2A (agent-to-agent) implementations
- Experience controlling or mitigating the impact of non-determinism in AI solutions
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